T. Tani, M. Sakoda, Kazuo Tanaka (1992)
Fuzzy modeling by ID3 algorithm and its application to prediction of heater outlet temperature[1992 Proceedings] IEEE International Conference on Fuzzy Systems
P. Scheunders (1997)
A genetic c-Means clustering algorithm applied to color image quantizationPattern Recognit., 30
E. Pimenta, João Gama (2005)
A study on Error Correcting Output Codes2005 portuguese conference on artificial intelligence
T. Dasu, T. Johnson (2003)
Exploratory Data Mining and Data Cleaning
G. Deboeck (1998)
Picking Mutual Funds with Self-Organizing Maps
E. Backer (1995)
Computer-assisted reasoning in cluster analysis
L. Jain, V. Vemuri (1998)
Industrial Applications of Neural Networks
Kuan-Yu Chen, Cheng-Hua Wang (2007)
Support vector regression with genetic algorithms in forecasting tourism demandTourism Management, 28
Dr. Spears (2000)
Evolutionary Algorithms
S. Bandyopadhyay, U. Maulik (2002)
An evolutionary technique based on K-Means algorithm for optimal clustering in RNInf. Sci., 146
N. Cristianini, J. Shawe-Taylor (2000)
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
J. Quinlan (1992)
C4.5: Programs for Machine Learning
W. Greene (2003)
Unsupervised hierarchical clustering via a genetic algorithmThe 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2
T. Novak, D. Hoffman, Y. Yung (2000)
Measuring the Customer Experience in Online Environments: A Structural Modeling ApproachMarketing Science, 19
S. Bhattacharyya (1998)
Direct Marketing Response Models Using Genetic Algorithms
L. Kaufman, P. Rousseeuw (1991)
Finding Groups in Data: An Introduction to Cluster Analysis
W. Remus, M. O'Connor (2001)
Neural Networks for Time-Series Forecasting
P. Lisboa, A. Vellido, Bill Edisbury (2000)
Business Applications of Neural Networks:The State-of-the-Art of Real-World Applications
Deepak Agrawal, Christopher Schorling (1996)
Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit modelJournal of Retailing, 72
G. Zhang, M. Qi (2002)
Predicting Consumer Retail Sales Using Neural Networks
C. Olaru, L. Wehenkel (2003)
A complete fuzzy decision tree techniqueFuzzy Sets Syst., 138
K. Deb, D. Goldberg (1989)
An Investigation of Niche and Species Formation in Genetic Function Optimization
V. Vapnik (2000)
The Nature of Statistical Learning Theory
P. Werbos (1974)
Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
U. Kressel (1999)
Pairwise classification and support vector machines
C. Park, Sandra Milberg, R. Lawson (1991)
Evaluation of Brand Extensions: The Role of Product Feature Similarity and Brand Concept ConsistencyJournal of Consumer Research, 18
Michael Clarke, R. Kruse, S. Moral (1991)
Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
A. Moser, M. Murty (2000)
On the Scalability of Genetic Algorithms to Very Large-Scale Feature Selection
J. Bezdek, Srinivas Boggavarapu, L. Hall, A. Bensaid (1994)
Genetic algorithm guided clusteringProceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence
K. Smith‐Miles, R. Willis, M. Brooks (2000)
An analysis of customer retention and insurance claim patterns using data mining: a case studyJournal of the Operational Research Society, 51
R. Bose, D. Ray-Chaudhuri (1960)
On A Class of Error Correcting Binary Group CodesInf. Control., 3
J. Baldwin, Dong Xie (2004)
Simple Fuzzy Logic Rules Based on Fuzzy Decision Tree for Classification and Prediction Problem
R. Law (2000)
Back-propagation learning in improving the accuracy of neural network-based tourism demand forecastingTourism Management, 21
J. Steenkamp, H. Baumgartner (2000)
On the use of structural equation models for marketing modelingInternational Journal of Research in Marketing, 17
Isabelle Guyon, A. Elisseeff (2003)
An Introduction to Variable and Feature SelectionJ. Mach. Learn. Res., 3
Kevin Cherkauer, J. Shavlik (1996)
Growing Simpler Decision Trees to Facilitate Knowledge Discovery
Meghan Miller, A. Jerebko, J. Malley, R. Summers (2003)
Feature selection for computer-aided polyp detection using genetic algorithms, 5031
Estevam Hruschka, Eduardo Hruschka, N. Ebecken (2003)
A feature selection bayesian approach for extracting classification rules with a clustering genetic algorithmApplied Artificial Intelligence, 17
J. Bala, Jeffrey Huang, H. Vafaie, K. Jong, H. Wechsler (1995)
Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification
D. Rumelhart, Geoffrey Hinton, Ronald Williams (1986)
Learning internal representations by error propagation
J. Handl, Joshua Knowles (2004)
Evolutionary Multiobjective Clustering
Marc Cowgill, Robert Harvey, Layne Watson (1998)
A Genetic Algorithm Approach to Cluster AnalysisComputers & Mathematics With Applications, 37
F. Masulli, G. Valentini (2000)
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
B. Ripley (1993)
Statistical aspects of neural networks
Anil Jain, R. Dubes (1988)
Algorithms for Clustering Data
J. Grefenstette (1997)
Proportional selection and sampling algorithms
C. Coello, G. Lamont (2004)
Applications Of Multi-Objective Evolutionary Algorithms
B. Wallet, D. Marchette, J. Solka (1996)
Matrix representation for genetic algorithms, 2756
J.-S.R. Jang (1994)
Structure determination in fuzzy modeling: a fuzzy CART approachProceedings of 1994 IEEE 3rd International Fuzzy Systems Conference
Wenjing Li, Tong Lee (2002)
Object recognition and articulated object learning by accumulative Hopfield matchingPattern Recognit., 35
F. Chen, Shu-Fan Liu (2000)
A neural-network approach to recognize defect spatial pattern in semiconductor fabricationIEEE Transactions on Semiconductor Manufacturing, 13
T. Roh (2006)
Forecasting the volatility of stock price index
P. Tan, M. Steinbach, Vipin Kumar (2005)
Introduction to Data Mining
G. Carpenter, S. Grossberg (1991)
Pattern Recognition by Self-Organizing Neural NetworksPsyccritiques, 40
C. Bishop (1995)
Neural networks for pattern recognition
F. Divina, E. Marchiori (2005)
Handling continuous attributes in an evolutionary inductive learnerIEEE Transactions on Evolutionary Computation, 9
Bin Ni, Juan Liu (2004)
A Novel Method of Searching the Microarray Data for the Best Gene Subsets by Using a Genetic Algorithm
M. Smith, L. Bull (2004)
Using Genetic Programming for Feature Creation with a Genetic Algorithm Feature Selector
Johannes Fürnkranz (1999)
Separate-and-Conquer Rule LearningArtificial Intelligence Review, 13
G. Rätsch, Alex Smola, S. Mika (2002)
Adapting Codes and Embeddings for Polychotomies
S. Geman, E. Bienenstock, R. Doursat (1992)
Neural Networks and the Bias/Variance DilemmaNeural Computation, 4
K. Hornik, M. Stinchcombe, H. White (1989)
Multilayer feedforward networks are universal approximatorsNeural Networks, 2
F. Divina, E. Marchiori (2002)
Evolutionary Concept Learning
Thomas Dietterich, Ghulum Bakiri (1994)
Solving Multiclass Learning Problems via Error-Correcting Output CodesArXiv, cs.AI/9501101
David Luna, L. Peracchio, M. Juan (2002)
Cross-cultural and cognitive aspects of web site navigationJournal of the Academy of Marketing Science, 30
D. Carvalho, A. Freitas (2004)
A hybrid decision tree/genetic algorithm method for data miningInf. Sci., 163
H. Prade, G. Richard, M. Serrurier (2003)
Enriching Relational Learning with Fuzzy Predicates
E. Cantú-Paz, J. Foster, K. Deb, L. Davis, R. Roy, Una-May O’Reilly, H. Beyer, R. Standish, G. Kendall, Stewart Wilson, M. Harman, J. Wegener, D. Dasgupta, M. Potter, A. Schultz, K. Dowsland, N. Jonoska, J. Miller (2003)
Genetic and Evolutionary Computation — GECCO 2003, 2723
N. Pal, J. Bezdek (1995)
On cluster validity for the fuzzy c-means modelIEEE Trans. Fuzzy Syst., 3
E. Zitzler, M. Laumanns, L. Thiele (2001)
SPEA2: Improving the strength pareto evolutionary algorithm, 103
O. Dekel, Y. Singer (2002)
Multiclass Learning by Probabilistic Embeddings
S. Miyake, F. Kanaya (1991)
A neural network approach to a Bayesian statistical decision problemIEEE transactions on neural networks, 2 5
G. Klir, B. Yuan (1995)
Fuzzy sets and fuzzy logic - theory and applications
G. Pappa, A. Freitas (2008)
Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming
A. Freitas (2001)
Understanding the Crucial Role of Attribute Interaction in Data MiningArtificial Intelligence Review, 16
Juan Liu, J. Kwok (2000)
An extended genetic rule induction algorithm
J. Koza (1993)
Genetic programming - on the programming of computers by means of natural selection
G. Deboeck, T. Kohonen (1998)
Visual Explorations in Finance with Self-Organizing Maps
P. Fränti, J. Kivijärvi, T. Kaukoranta, O. Nevalainen (1997)
Genetic Algorithms for Large-Scale Clustering ProblemsComput. J., 40
George Cybenko (1989)
Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals and Systems, 2
E. Tapia, José González, Javier García-Villalba (2003)
Good Error Correcting Output Codes for Adaptive Multiclass Learning
M. Kiang, U. Kulkarni, K. Tam (1995)
Self-organizing map network as an interactive clustering tool - An application to group technologyDecis. Support Syst., 15
A. Aho, Ravi Sethi, J. Ullman (1986)
Compilers: Principles, Techniques, and Tools
S. Gunn (1998)
Support Vector Machines for Classification and Regression
J. Dunn (1973)
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, 3
Sung-Hwan Min, Jumin Lee, Ingoo Han (2006)
Hybrid genetic algorithms and support vector machines for bankruptcy predictionExpert Syst. Appl., 31
D. Dubois, H. Prade, T. Sudkamp (2005)
On the representation, measurement, and discovery of fuzzy associationsIEEE Transactions on Fuzzy Systems, 13
Weiguo Sheng, Xiaohui Liu (2004)
A hybrid algorithm for k-medoid clustering of large data setsProceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), 1
A. Ciampi, Y. Lechevallier (1997)
Statistical models as building blocks of neural networksCommunications in Statistics-theory and Methods, 26
Donald Jones, M. Beltramo (1991)
Solving Partitioning Problems with Genetic Algorithms
E. Alba, F. Chicano (2004)
Solving the error correcting code problem with parallel hybrid heuristics
Takao Teranol, Y. Ishino (1998)
Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis
M. Adya, Fred Collopy (1998)
How effective are neural networks at forecasting and prediction? A review and evaluationJournal of Forecasting, 17
Mineichi Kudo, J. Sklansky (2000)
Comparison of algorithms that select features for pattern classifiersPattern Recognit., 33
K. Deb (2001)
Multi-objective optimization using evolutionary algorithms
C. Murthy, N. Chowdhury (1996)
In search of optimal clusters using genetic algorithmsPattern Recognit. Lett., 17
J. Shim, Merrill Warkentin, J. Courtney, D. Power, R. Sharda, C. Carlsson (2002)
Past, present, and future of decision support technologyDecis. Support Syst., 33
D. Goldberg (1988)
Genetic Algorithms in Search Optimization and Machine Learning
Peter Clark, Robin Boswell (1991)
Rule Induction with CN2: Some Recent Improvements
R. Fletcher (1988)
Practical Methods of Optimization
Girish Punj, D. Stewart (1983)
Cluster Analysis in Marketing Research: Review and Suggestions for ApplicationJournal of Marketing Research, 20
Z. Michalewicz, D. Fogel (2004)
How to Solve It: Modern HeuristicsHow to Solve It: Modern Heuristics
S. Shumsky, A. Yarovoy (1998)
Self-Organizing Atlas of Russian Banks
W. McCulloch, W. Pitts (1990)
A logical calculus of the ideas immanent in nervous activityBulletin of Mathematical Biology, 52
A. Berger (1999)
ERROR-CORRECTING OUTPUT CODING FOR TEXT CLASSIFICATION
B. Cheng, D. Titterington (1994)
Neural Networks: A Review from a Statistical PerspectiveStatistical Science, 9
C. Janikow (1998)
Fuzzy decision trees: issues and methodsIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 28 1
Russell Reed (1993)
Pruning algorithms-a surveyIEEE transactions on neural networks, 4 5
W. Banzhaf, F. Francone, Robert Keller, P. Nordin (1998)
Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications
K. Krawiec (2002)
Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery TasksGenetic Programming and Evolvable Machines, 3
M. Drobics, Ulrich Bodenhofer, E. Klement (2003)
FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptionsInt. J. Approx. Reason., 32
J. Levy, Eunsang Yoon (1995)
Modeling global market entry decision by fuzzy logic with an application to country risk assessmentEuropean Journal of Operational Research, 82
A. Rozsypal, M. Kubát (2003)
Selecting representative examples and attributes by a genetic algorithmIntell. Data Anal., 7
Ethem Alpaydin, E. Mayoraz (1999)
Learning error-correcting output codes from data, 2
H. Dia (2001)
An object-oriented neural network approach to short-term traffic forecastingEur. J. Oper. Res., 131
D. Goldberg, J. Richardson (1987)
Genetic Algorithms with Sharing for Multimodalfunction Optimization
G. Pappa, A. Freitas (2006)
Automatically Evolving Rule Induction Algorithms
Wesley Romão, A. Freitas, Itana Gimenes (2004)
Discovering interesting knowledge from a science and technology database with a genetic algorithmAppl. Soft Comput., 4
J. Holland (1975)
Adaptation in natural and artificial systems
Dong Song, M. Heywood, A. Zincir-Heywood (2005)
Training genetic programming on half a million patterns: an example from anomaly detectionIEEE Transactions on Evolutionary Computation, 9
X. Gandibleux, M. Sevaux, K. Sörensen, V. T’kindt (2004)
Metaheuristics for Multiobjective Optimisation, 535
O. Lewis, J. Ware, D. Jenkins (1997)
A novel neural network technique for the valuation of residential propertyNeural Computing & Applications, 5
K. Deb (2000)
An Efficient Constraint Handling Method for Genetic AlgorithmsComputer Methods in Applied Mechanics and Engineering, 186
J. Lawry (2004)
A framework for linguistic modellingArtif. Intell., 155
Anil Jain, M. Murty, P. Flynn (1999)
Data clustering: a reviewACM Comput. Surv., 31
M. Qi (2001)
Predicting US recessions with leading indicators via neural network modelsInternational Journal of Forecasting, 17
D. Hebb (1988)
The organization of behavior
A. Freitas (2004)
A critical review of multi-objective optimization in data mining: a position paperSIGKDD Explor., 6
Yongguo Liu, Kefei Chen, Xiaofeng Liao, Wei Zhang (2004)
A genetic clustering method for intrusion detectionPattern Recognit., 37
Thomas Reutterer, M. Natter (2000)
Segmentation-based competitive analysis with MULTICLUS and topology representing networksComput. Oper. Res., 27
M. Wong, K. Leung (2000)
Data Mining Using Grammar Based Genetic Programming and Applications
E. Azoff (1994)
Neural Network Time Series: Forecasting of Financial Markets
Marcus Frean (1990)
The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural NetworksNeural Computation, 2
S. Mitra, Y. Hayashi (2000)
Neuro-fuzzy rule generation: survey in soft computing frameworkIEEE transactions on neural networks, 11 3
H. Vafaie, K. Jong (1998)
Evolutionary Feature Space Transformation
T. Terano, M. Inada (2003)
Data mining from clinical data using interactive evolutionary computation
Anna Tulkki (1998)
Real Estate Investment Appraisal of Buildings using SOM
A. Klautau, N. Jevtic, A. Orlitsky (2003)
On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector MachinesJ. Mach. Learn. Res., 4
Yuhai Wu (2021)
Statistical Learning TheoryTechnometrics, 41
T. Windeatt, R. Ghaderi (2003)
Coding and decoding strategies for multi-class learning problemsInf. Fusion, 4
J. Casillas, F. Martínez-López, F. Martínez (2004)
Fuzzy Association Rules For Estimating Consumer Behaviour Models And Their Its Application To Explaining Trust In Internet ShoppingFuzzy economic review, 09
John Atkinson-Abutridy, C. Mellish, S. Aitken (2003)
A semantically guided and domain-independent evolutionary model for knowledge discovery from textsIEEE Trans. Evol. Comput., 7
Sergio Escalera, O. Pujol, P. Radeva (2006)
Decoding of Ternary Error Correcting Output Codes
Niall O'Connor, M. Madden (2005)
A neural network approach to predicting stock exchange movements using external factors
L. Kuncheva (2005)
Using diversity measures for generating error-correcting output codes in classifier ensemblesPattern Recognit. Lett., 26
V. Vapnik (2006)
Estimation of Dependences Based on Empirical DataEstimation of Dependences Based on Empirical Data
M. Beynon, B. Curry, P. Morgan (2001)
Knowledge discovery in marketing: An approach through Rough Set TheoryEuropean Journal of Marketing, 35
J. Baldwin, T. Martin, B. Pilsworth (1995)
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
R. Agrawal, T. Imielinski, A. Swami (1993)
Mining association rules between sets of items in large databasesACM SIGMOD Record, 22
Melanie Mitchell (1996)
An introduction to genetic algorithms
T. Caliński, J. Harabasz (1974)
A dendrite method for cluster analysisCommunications in Statistics-theory and Methods, 3
Zengchang Qin, J. Lawry (2007)
Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees
A. Freitas (2005)
Evolutionary Algorithms for Data Mining
G. Deboeck (1998)
Investment Maps of Emerging Markets
K. Dontas, K. Jong (1990)
Discovery of maximal distance codes using genetic algorithms[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence
Peter Flach (2003)
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics
S. Knerr, L. Personnaz, G. Dreyfus (1989)
Single-layer learning revisited: a stepwise procedure for building and training a neural network
R. Caruana, Alexandru Niculescu-Mizil (2004)
Data mining in metric space: an empirical analysis of supervised learning performance criteriaProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Geoffrey Hinton (1992)
How neural networks learn from experience.Scientific American, 267 3
R. Horn, Charles Johnson (1985)
Matrix analysis
Dr. Freitas (2002)
Data Mining and Knowledge Discovery with Evolutionary Algorithms
M. Smith, L. Bull (2003)
Feature Construction and Selection Using Genetic Programming and a Genetic Algorithm
Y. Freund, R. Schapire (1997)
A decision-theoretic generalization of on-line learning and an application to boosting
S. Fahlman, C. Lebiere (1989)
The Cascade-Correlation Learning Architecture
S. Haykin (1998)
Neural Networks: A Comprehensive Foundation
(1998)
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
J. Bala, K. Jong, Jeffrey Huang, H. Vafaie, H. Wechsler (1996)
Using Learning to Facilitate the Evolution of Features for Recognizing Visual ConceptsEvolutionary Computation, 4
D. Rumelhart, D. Zipser (1986)
Feature discovery by competitive learning
A. Giordana, F. Neri (1995)
Search-Intensive Concept InductionEvolutionary Computation, 3
E. Falkenauer (1998)
Genetic Algorithms and Grouping Problems
Ying Dai, Y. Nakano (1997)
Recognition of facial images with low resolution using a hopfield memory model
S. Knerr, L. Personnaz, G. Dreyfus (1992)
Handwritten digit recognition by neural networks with single-layer trainingIEEE transactions on neural networks, 3 6
Terence Sanger, P. Baljekar (1958)
The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review, 65 6
F. Provost, Tom Fawcett, Ron Kohavi (1998)
The Case against Accuracy Estimation for Comparing Induction Algorithms
H. Zimmermann (1985)
Fuzzy Set Theory - and Its Applications
Krishna Kummamuru, M. Murty (1999)
Genetic K-means algorithmIEEE Trans. Syst. Man Cybern. Part B, 29
Jihoon Yang, Vasant Honavar (1998)
Feature Subset Selection Using a Genetic AlgorithmIEEE Intell. Syst., 13
Ken-ichi Funahashi (1998)
Multilayer neural networks and Bayes decision theoryNeural networks : the official journal of the International Neural Network Society, 11 2
R. Krovi (1992)
Genetic algorithms for clustering: a preliminary investigationProceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, iv
G. Pappa, A. Freitas, Celso Kaestner (2004)
Multi-Objective Algorithms for Attribute Selection in Data Mining
J. Hopfield (1982)
Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences of the United States of America, 79 8
Lawry Jonathan, Shanahan Jimi, Ralescu Anca (2003)
Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
M. Csíkszentmihályi (1975)
Play and Intrinsic RewardsJournal of Humanistic Psychology, 15
J. Kivijärvi, P. Fränti, O. Nevalainen (2003)
Self-Adaptive Genetic Algorithm for ClusteringJournal of Heuristics, 9
J. Quinlan (1986)
Induction of Decision TreesMachine Learning, 1
J. Backus, F. Bauer, Julien Green, C. Katz, J. McCarthy, A. Perlis, H. Rutishauser, K. Samelson, B. Vauquois, J. Wegstein, A. Wijngaarden, M. Woodger, P. Naur (1963)
Revised report on the algorithm language ALGOL 60Commun. ACM, 6
(2002)
Computing , Artificial Intelligence and Information Technology Predicting information technology project escalation : A neural network approach
P. Brockett, Xiaohua Xia, R. Derrig (1998)
Using Kohonen's Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims FraudJournal of Risk and Insurance, 65
Stephen Chen, Cesar Guerra-Salcedo, Stephen Smith (1999)
Non-Standard Crossover for a Standard Representation - Commonality-Based Feature Subset Selection
Johannes Fürnkranz, Peter Flach (2003)
An Analysis of Rule Evaluation Metrics
G. Lim, M. Alder, P. Hadingham (1991)
Adaptive quadratic neural nets[Proceedings] 1991 IEEE International Joint Conference on Neural Networks
G. Zhang (2007)
Avoiding Pitfalls in Neural Network ResearchIEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37
YongSeog Kim, W. Street, F. Menczer (2000)
Feature selection in unsupervised learning via evolutionary search
Shaw Chen, P. Mangiameli, D. West (1995)
The comparative ability of self-organizing neural networks to define cluster structureOmega-international Journal of Management Science, 23
David Morrison, R. Snow, John Lamoureux (1982)
Advances in Process ControlScience, 215
Kees Jong, E. Marchiori, M. Sebag (2004)
Ensemble Learning with Evolutionary Computation: Application to Feature Ranking
P. Rousseeuw, A. Leroy (2005)
Wiley Series in Probability and Mathematical Statistics
P. West, P. Brockett, L. Golden (1997)
A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer ChoiceMarketing Science, 16
D. Enke, Suraphan Thawornwong (2005)
The use of data mining and neural networks for forecasting stock market returnsExpert Syst. Appl., 29
V. Prybutok, Junsub Yi, David Mitchell (2000)
Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrationsEur. J. Oper. Res., 122
E. Korkmaz, Jun Du, R. Alhajj, K. Barker (2006)
Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clusteringIntell. Data Anal., 10
R. Cole (1998)
Clustering with genetic algorithms
Peter Clark, T. Niblett (1989)
The CN2 Induction AlgorithmMachine Learning, 3
T. Kohonen (2004)
Self-organized formation of topologically correct feature mapsBiological Cybernetics, 43
J. Hopfield, D. Tank (1985)
“Neural” computation of decisions in optimization problemsBiological Cybernetics, 52
Jukka Hekanaho (1995)
Symbiosis in Multimodal Concept Learning
K. Smith‐Miles, Alan Ng (2003)
Web page clustering using a self-organizing map of user navigation patternsDecis. Support Syst., 35
M. Simon, J. Pulido, M. Rodríguez, J. Perez, J. Criado (2006)
A genetic algorithm to design error correcting codes
Hosun Rhim, Lee Cooper (2005)
Assessing potential threats to incumbent brands: New product positioning under price competition in a multisegmented marketInternational Journal of Research in Marketing, 22
David Davies, D. Bouldin (1979)
A Cluster Separation MeasureIEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1
M. Qi, G. Zhang (2001)
An investigation of model selection criteria for neural network time series forecastingEur. J. Oper. Res., 132
Huan Liu, H. Motoda (1998)
Feature Selection for Knowledge Discovery and Data Mining, 454
F. Otero, Monique Silva, A. Freitas, J. Nievola (2002)
Genetic Programming for Attribute Construction in Data Mining
R. Michalski (1969)
On the Quasi-Minimal Solution of the General Covering Problem
D. West (2000)
Neural network credit scoring modelsComput. Oper. Res., 27
Jianping Zhang (1992)
Selecting Typical Instances in Instance-Based Learning
T. Baeck, D. Fogel, Z. Michalewicz (2000)
Evolution-ary Computation 1: Basic Algorithms and Operators
M. Dittenbach, A. Rauber, D. Merkl (2002)
Uncovering hierarchical structure in data using the growing hierarchical self-organizing mapNeurocomputing, 48
B. Liu, W. Hsu, Shu Chen (1997)
Using General Impressions to Analyze Discovered Classification Rules
A. Giordana, L. Saitta, F. Zini (1994)
Learning Disjunctive Concepts by Means of Genetic Algorithms
K. Crammer, Y. Singer (2002)
On the Learnability and Design of Output Codes for Multiclass ProblemsMachine Learning, 47
Catherine Blake (1998)
UCI Repository of machine learning databases
K. Deb, S. Agrawal, Amrit Pratap, T. Meyarivan (2002)
A fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans. Evol. Comput., 6
Hongxing He, Jin-cheng Wang, W. Graco, S. Hawkins (1997)
Application of neural networks to detection of medical fraudExpert Systems With Applications, 13
S. Changchien, T. Lu (2001)
Mining association rules procedure to support on-line recommendation by customers and products fragmentationExpert Syst. Appl., 20
Joanna Wallis, S. Houghten (2002)
A COMPARATIVE STUDY OF SEARCH TECHNIQUES APPLIED TO THE MINIMUM DISTANCE PROBLEM OF BCH CODES
Yaochu Jin (2006)
Multi-Objective Machine Learning, 16
B. Iglesia (2007)
Application of multi-objective metaheuristic algorithms in data mining
M. Collins, R. Schapire, Y. Singer (2000)
Logistic Regression, AdaBoost and Bregman DistancesMachine Learning, 48
C. Darwin, M. Peckham (2006)
The Origin of Species: A Variorum Text
P. Doganis, A. Alexandridis, Panagiotis Patrinos, H. Sarimveis (2006)
Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computingJournal of Food Engineering, 75
L. Hall, I. Ozyurt, J. Bezdek (1999)
Clustering with a genetically optimized approachIEEE Trans. Evol. Comput., 3
H. Petersohn (1998)
Assessment of Clusteranalysis and Self-Organizing MapsInt. J. Uncertain. Fuzziness Knowl. Based Syst., 6
E. Carlson (1998)
Real Estate Investment Appraisal of Land Properties using SOM
G. Zhang, Michael Hu, B. Patuwo, Daniel Indro (1999)
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysisEur. J. Oper. Res., 116
J. Baldwin, J. Lawry, T. Martin (1997)
Mass assignment fuzzy ID3 with applications
Eduardo Hruschka, N. Ebecken (2003)
A genetic algorithm for cluster analysisIntell. Data Anal., 7
Arantza Casillas, Mayte Lena, Raquel Martínez-Unanue (2003)
Document Clustering into an Unknown Number of Clusters Using a Genetic Algorithm
C. Groot, D. Würtz (1991)
Analysis of univariate time series with connectionist nets: A case study of two classical examplesNeurocomputing, 3
Wei Zhang, Q. Cao, M. Schniederjans (2004)
Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative MethodsDecis. Sci., 35
K. Spackman (1989)
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning
L. Tseng, Shiueng-Bien Yang (2001)
A genetic approach to the automatic clustering problemPattern Recognit., 34
A. Freitas, S. Lavington (1997)
Mining Very Large Databases with Parallel Processing, 9
J. Lawry (2006)
Modelling and Reasoning with Vague Concepts, 12
T. Hastie, R. Tibshirani (1997)
Classification by Pairwise Coupling
Tao Wang, X. Zhuang, X. Xing (1992)
Robust segmentation of noisy images using a neural network modelImage Vis. Comput., 10
Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results. ; This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results. ; Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook. This book was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results. Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management. ; Neural Network Methods.- to Soft Computing for Knowledge Discovery and Data Mining.- Neural Networks For Data Mining.- Improved SOM Labeling Methodology for Data Mining Applications.- Evolutionary Methods.- A Review of evolutionary Algorithms for Data Mining.- Genetic Clustering for Data Mining.- Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming.- evolutionary Design of Code-matrices for Multiclass Problems.- Fuzzy Logic Methods.- The Role of Fuzzy Sets in Data Mining.- Support Vector Machines and Fuzzy Systems.- KDD in Marketing with Genetic Fuzzy Systems.- Knowledge Discovery in a Framework for Modelling with Words.- Advanced Soft Computing Methods and Areas.- Swarm Intelligence Algorithms for Data Clustering.- A Diffusion Framework for Dimensionality Reduction.- Data Mining and Agent Technology: a fruitful symbiosis.- Approximate Frequent Itemset Mining In the Presence of Random Noise.- The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining.; Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook. The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic. The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology. This book was written to provide investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including marketing, manufacturing, medical, and others, but it also includes various real-world case studies with detailed results. Soft Computing for Knowledge Discovery and Data Mining is designed for theoreticians, researchers and advanced practitioners in industry. Practitioners may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a textbook or reference for advanced-level students in mathematical quantitative methods in the above fields. About the editors: Oded Maimon is Full Professor at the Department of Industrial Engineering, Tel-Aviv University, Israel. Lior Rokach is Assistant Professor at the Department of Information System Engineering, Ben-Gurion University of the Negev, Israel. Maimon and Rokach are recognized international experts in data mining and business intelligence, and serve in leading positions in this field. They have written numerous scientific articles and are the editors of the complete Data Mining and Knowledge Discovery Handbook (2005). They have jointly authored two of the best detailed books in the field of data mining: Decomposition Methodology for Knowledge Discovery and Data Mining (2005), and Data Mining with Decision Trees (2007). ; Illustrations of various applications including manufacturing, medical, banking, insurance and others Includes various real-world case studies with detailed results Edited by the highly-regarded editors of the "Data Mining and Knowledge Discovery Handbook" (2005, Springer) ; This book introduces soft computing methods that extend the envelope of problems that data mining can efficiently solve. It presents practical soft-computing approaches in data mining, including various real-world case studies with detailed results and featuring illustrations of such applications as manufacturing, medical, banking, insurance and others. Soft Computing for Knowledge Discovery and Data Mining was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. The book is also suitable for advanced-level students in computer science. ; US
Published: Oct 25, 2007
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote