An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization

被引：50
|
作者：
Ganapathy, S. [1 ]
Sethukkarasi, R. [1 ]
Yogesh, P. [1 ]
Vijayakumar, P. [2 ]
Kannan, A. [1 ]
机构：
[1] Anna Univ, Dept Informat Sci & Technol, Madras 600025, Tamil Nadu, India
[2] Univ Coll Engn, Dept Comp Sci & Engn, Tindivanam 604001, Villupuram, India
来源：
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES | 2014年 / 39卷 / 02期
关键词：
Temporal fuzzy min-max (TFMM) neural network; particle swarm optimization algorithm (PSOA); pattern classification; rule extraction; NEURAL-NETWORK; EXTRACTION;
D O I：
10.1007/s12046-014-0236-7
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min-Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min-Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the ten-fold cross validation.
引用
收藏
页码：283 / 302
页数：20
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