Please use this identifier to cite or link to this item:

Title: A rule-based classification algorithm: A rough set approach
Authors: Liao, C.-C.;Hsu, K.-W.
Contributors: 資科系
Keywords: Attribute-value pairs;Classification algorithm;Classification performance;Decision rules;Indiscernibility;Interpretability;Matrix methods;Nominal datum;Rough set;Rule generation method;Rule induction;Rule-based classification;separate-and-conquer;Understandability;Algorithms;Artificial intelligence;Classification (of information);Learning systems;Rough set theory;Separation;Data mining
Date: 2012
Issue Date: 2015-04-10 15:35:10 (UTC+8)
Abstract: In this paper, we propose a rule-based classification algorithm named ROUSER (ROUgh SEt Rule). Researchers have proposed various classification algorithms and practitioners have applied them to various application domains, while most of the classification algorithms are designed with a focus on classification performance rather than interpretability or understandability of the models built using the algorithms. ROUSER is specifically designed to extract human understandable decision rules from nominal data. What distinguishes ROUSER from most, if not all, other rule-based classification algorithms is that it utilizes a rough set approach to decide an attribute-value pair for the antecedents of a rule. Moreover, the rule generation method of ROUSER is based on the separate-and-conquer strategy, and hence it is more efficient than the indiscernibility matrix method that is widely adopted in the classification algorithms based on the rough set theory. On about half of the data sets considered in experiments, ROUSER can achieve better classification performance than do classification algorithms that are able to generate decision rules or trees. © 2012 IEEE.
Relation: Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012,1-5
Data Type: conference
DOI 連結:
Appears in Collections:[資訊科學系] 會議論文

Files in This Item:

File Description SizeFormat

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing