Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision MakingSharif, Samane; Akbarzadeh-T, Mohammad-R.
2021 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1978803
INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.
Solvability, Supersolvability and Schreier Refinement Theorem for L-SubgroupsAjmal, N.; Jahan, I.; Davvaz, B.
2021 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1997444
This paper is in continuation of our previous works. In this paper, we study solvable L-subgroups of an L-group and establish a level subset characterisation for the same. Then, this level subset characterisation has been used to describe solvability of L-subgroups with the help of the notions of normal and subinvariant series of L-subgroups. Moreover, the concept of supersolvable L-subgroups of an L-group has been introduced. It has been established that supersolvable L-groups are closed under the formation of subgroups. Also, commutator L-subgroup of a supersolvable L-subgroup is shown to be nilpotent. In the last, we extend Zassenhaus Lemma to L-setting and utilise it to establish a version of Schreier Refinement Theorem in L-group Theory.
Comparison and Evaluation of Built Environment Factors for Developing Pedestrian Urban TravelsNabipour, Mohammad; Nasseri, Seyed Hadi; Tavakoli Saber, Elnaz
2021 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.2002664
Summary: This study examines the impacts of the built environment on pedestrian urban travels using a fuzzy AHP approach, by taking into account fifteen different variables based on three criteria: network design, environment, and safety. We gathered data from academic and industry experts using a fuzzy-based pairwise comparative survey. Advantage: We adopt two methods for selecting high-priority variables. The average value of cumulative weights, which prioritise variables with a weight greater than the average value, and a variation weights values analysis that divides variables into three groups as high, medium, and low priority depending on the weight pattern slope’s breaking points. The findings indicate that the weights variation approach is more effective. Limit: Because the survey statistical population comprised both academic and industrial experts, a significant amount of effort was spent identifying qualified candidates and gathering the necessary data. Results: The results prioritise effective variables including level of stress, lighting, obstacles on sidewalks, width of sidewalk, sidewalk surface quality, pedestrian bridges, cleanness and density of green areas, access to public transportation, intersection traffic controls, and walking utilities. Furthermore, the findings show that by growing policies on the variables of high and medium priority, up to 68 percent of the objective function can be achieved pedestrian urban commuting will significantly improve.
Optimisation of Thresholds in Probabilistic Rough Sets with Artificial Bee Colony AlgorithmSoumya, T. V.; Sabu, M. K.
2021 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.2002665
The Probabilistic Rough Sets (PRS) theory determines the certainty of an object's inclusion into a class, resulting in the division of the entire data set into three regions under a concept. These regions, namely the positive, negative and boundary regions, are generated using an evaluation function and threshold values. The threshold optimisation and the construction and interpretation of an evaluation function offer various methods in the background. Even though most of the methods in the PRS follow an iterative strategy, they lack a common framework, usually affecting the comparison and overall performance evaluation among these methods. This proposed work aims to minimise the uncertainty in three regions via optimising the thresholds using the Artificial Bee Colony (ABC) algorithm. The ABC algorithm is adapted to generate a common framework that results in different optimal pairs of thresholds with a minimum number of iterations. By considering the probabilistic information about an equivalence class structure, we compare the results obtained from the proposed approach with the state-of-the-art methods like Information-Theoretic Rough Sets, Game-Theoretic Rough sets and Genetic Algorithm-based optimisation. The results reveal that the proposed algorithm outperforms existing techniques and leads to a superior method for threshold optimisation in the PRS.