Open AccessClustering-based rule generation methods for fuzzy classifier using Autonomous Data Partitioning algorithm
Author(s) -
Mikhail Svetlakov,
I. A. Hodashinsky
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1989/1/012032
Subject(s) - cluster analysis , computer science , data mining , classifier (uml) , fuzzy clustering , fuzzy logic , algorithm , parametric statistics , artificial intelligence , pattern recognition (psychology) , mathematics , statistics
In this paper, clustering-based rule generation methods for fuzzy classifier using non-parametric Autonomous Data Partitioning algorithm have been proposed. ADP-algorithm is used to determine the number of clusters for use in various k-means-like clustering algorithms. Proposed method contributes to solving the problem of determining optimal number of clusters/rules. The efficiency of fuzzy classifiers with rules constructed by the specified algorithms has been tested on data sets from the KEEL repository. Experimental results show that proposed method outperforms baseline algorithm (the extremums rulebase generation algorithm) both in terms of classification accuracy and geometric mean metrics.