Access the full text.
Sign up today, get DeepDyve free for 14 days.
(1963)
Distribution-free multiple comparison
N. Lavrač, Peter Flach, B. Zupan (1999)
Rule Evaluation Measures: A Unifying View
T. Joachims (1998)
Text Categorization with Support Vector Machines: Learning with Many Relevant Features
B. Liu, W. Hsu, Y. Ma (1998)
Integrating Classification and Association Rule Mining
J. Quinlan (1983)
Learning Efficient Classification Procedures and Their Application to Chess End Games
Elena Baralis, S. Chiusano, P. Garza (2008)
A Lazy Approach to Associative ClassificationIEEE Transactions on Knowledge and Data Engineering, 20
J. Quinlan (1992)
C4.5: Programs for Machine Learning
Monowar Bhuyan, D. Bhattacharyya, Hossein Homaei, Hamid Shahriari, Jiankun Hu, Yong Yu, Yi Mu, Guilin Wang, Ying Sun, Hassan Asghar, Josef Pieprzyk, Huaxiong Wang, Jun Zhang, Yang Xiang, Wanlei Zhou, Lei Ye, Lan Zhou, Vijay Varadharajan, M. Hitchens, Lein Harn, Chia-Yin Lee, Changlu Lin, Chin-Chen Chang, Yini Wang, Sheng Wen, Silvio Cesare, Erika Rosas, Olivier Marin, Xavier Bonnaire (1958)
The Computer JournalNature, 181
Ke Wang, Senqiang Zhou, Yu He (2000)
Growing decision trees on support-less association rules
T. Cover, P. Hart (1967)
Nearest neighbor pattern classificationIEEE Trans. Inf. Theory, 13
Frans Coenen, P. Leng (2004)
An evaluation of approaches to classification rule selectionFourth IEEE International Conference on Data Mining (ICDM'04)
J. Quinlan, R. Cameron-Jones (1993)
FOIL: A Midterm Report
M. Friedman (1940)
A Comparison of Alternative Tests of Significance for the Problem of $m$ RankingsAnnals of Mathematical Statistics, 11
J. Demšar (2006)
Statistical Comparisons of Classifiers over Multiple Data SetsJ. Mach. Learn. Res., 7
M. Pazzani, S. Mani, W. Shankle (1997)
Beyond Concise and Colorful: Learning Intelligible Rules
William Cohen (1995)
Fast Effective Rule Induction
Xiuzhen Zhang, Guozhu Dong, K. Ramamohanarao (2006)
KTDA: emerging patterns based data analysis systemAnn. UMCS Informatica, 4
(2007)
UCI Machine Learning Repository
U. Fayyad, K. Irani (1993)
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
F. Wilcoxon (1945)
Individual Comparisons by Ranking MethodsBiometrics, 1
Frans Coenen, P. Leng, Shakil Ahmed (2004)
Data structure for association rule mining: T-trees and P-treesIEEE Transactions on Knowledge and Data Engineering, 16
R. Agrawal, R. Srikant (1998)
Fast Algorithms for Mining Association Rules
F. Thabtah, P. Cowling, Yonghong Peng (2005)
MCAR: multi-class classification based on association ruleThe 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005.
Y. Wang, Qin Xin, Frans Coenen (2007)
A Novel Rule Weighting Approach in Classification Association Rule MiningSeventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)
S. García, F. Herrera (2008)
An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise ComparisonsJournal of Machine Learning Research, 9
Xiaoxin Yin, Jiawei Han (2003)
CPAR: Classification based on Predictive Association Rules
Jiawei Han, J. Pei, Yiwen Yin (2000)
Mining frequent patterns without candidate generation
R. Duda, P. Hart (1974)
Pattern classification and scene analysis
S. Salzberg, Alberto Segre (1994)
Programs for Machine Learning
(2007)
Data mining software in java
Wenmin Li, Jiawei Han, J. Pei (2001)
CMAR: accurate and efficient classification based on multiple class-association rulesProceedings 2001 IEEE International Conference on Data Mining
Dimitris Meretakis, B. Wüthrich (1999)
Extending naïve Bayes classifiers using long itemsets
A new associative classification algorithm based on weighted voting (ACWV) is presented. ACWV takes advantage of two methods: the optimal rule method preferring high-quality rules and the voting method considering the majority of the rules. Moreover, the method takes into account both the length and convictions of rules to calculate their weights. First, ACWV builds a class-count FP-tree (called CCFP-tree) from the given historical data. After that, the weighted voting result for a new instance can be obtained from the CCFP-tree directly without storing, retrieving and sorting rules explicitly. The label of the class with maximal sum of weighted votes is then that of the new instance. Results of the experiments with 36 data sets selected from the UCI machine learning repository show that the proposed method has its advantages in comparison with previous methods in terms of classification accuracy.
The Computer Journal – Oxford University Press
Published: Jul 25, 2010
Keywords: classification association rule associative classification weighted voting
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
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.