Multi-objective evolutionary design of granular rule-based classifiers

被引:74
|
作者
Antonelli M. [1 ]
Ducange P. [2 ]
Lazzerini B. [3 ]
Marcelloni F. [3 ]
机构
[1] Translational Imaging Group, Centre for Medical Image Computing (CMIC), University College London, Wolfson House, Stephenson Way, London
[2] Faculty of Engineering, eCampus University, Via Isimbardi, 10, Novedrate
[3] Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, Pisa
关键词
Fuzzy sets; Granular rule-based classifiers; Intervals; Multi-objective evolutionary optimization;
D O I
10.1007/s41066-015-0004-z
中图分类号
学科分类号
摘要
In the last years, rule-based systems have been widely employed in several different application domains. The performance of these systems is strongly affected by the process of information granulation, which defines in terms of specific information granules such as sets, fuzzy sets and rough sets, the labels used in the rules. Generally, information granules are either provided by an expert, when possible, or extracted from the available data. In the framework of rule-based classifiers, we investigate the importance of determining an effective information granulation from data, preserving the comprehensibility of the granules. We show how the accuracies of rule-based classifiers can be increased by learning number and parameters of the granules, which partition the involved variables. To perform this analysis, we exploit a multi-objective evolutionary approach to the classifier generation we have recently proposed. We discuss different levels of information granulation optimization employing both the learning of the number of granules per variable and the tuning of each granule during the evolutionary process. We show and discuss the results obtained on several classification benchmark datasets using fuzzy sets and intervals as types of information granules. © 2015, Springer International Publishing Switzerland.
引用
收藏
页码:37 / 58
页数:21
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