A possibilistic rule-based classifier

Publication Type:
Conference Proceeding
Communications in Computer and Information Science, 2012, 297 CCIS (PART 1), pp. 21 - 31
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013007801OK.pdf Published version449.01 kB
Adobe PDF
Rule induction algorithms have gained a high popularity among machine learning techniques due to the intelligibility of their output, when compared to other black-box classification methods. However, they suffer from two main drawbacks when classifying test examples: i) the multiple classification problem when many rules cover an example and are associated with different classes, and ii) the choice of a default class, which concerns the non-covering case. In this paper we propose a family of Possibilistic Rule-based Classifiers (PRCs) to deal with such problems which are an extension and a modification of the Frank and Witten' PART algorithm. The PRCs keep the same rule learning step as PART, but differ in other respects. In particular, the PRCs learn fuzzy rules instead of crisp rules, consider weighted rules at deduction time in an unordered manner instead of rule lists. They also reduce the number of examples not covered by any rule, using a fuzzy rule set with large supports. The experiments reported show that the PRCs lead to improve the accuracy of the classical PART algorithm. © 2012 Springer-Verlag Berlin Heidelberg.
Please use this identifier to cite or link to this item: