Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification
作者： Ilya Hodashinsky , Konstantin Sarin , Alexander Shelupanov , Artem Slezkin
DOI: 10.3390/SYM11111423
关键词: Membership function 、 Classifier (UML) 、 Fuzzy rule 、 Feature selection 、 Swarm behaviour 、 Symmetric structure 、 Artificial intelligence 、 Fuzzy classification 、 Binary number 、 Pattern recognition 、 Computer science
摘要: This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage symmetric structure a membership function. Searching for (sub) optimal subset features is an NP-hard problem. In this paper, binary swallow swarm optimization (BSSO) algorithm feature selection proposed. To solve classification problem, we use fuzzy rule-based classifier. evaluate performance our method, BSSO compared to induction without some similar algorithms on well-known benchmark datasets. Experimental results show promising behavior proposed method in features.
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