A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data

被引：22

作者：

Ray, Shubhra Sankar ^{[1
]}

Ganivada, Avatharam ^{[2
]}

Pal, Sankar K. ^{[1
]}

机构：

[1] Indian Stat Inst, Machine Intelligence Unit, Ctr Soft Comp Res, Kolkata 700108, India

[2] Indian Stat Inst, Ctr Soft Comp Res, Kolkata 700108, India

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2016年 / 27卷 / 09期

关键词：

Bioinformatics; clustering; feature selection; granular neural network; rough-fuzzy computing; NEURAL-NETWORK; FUZZY; CLASSIFICATION; CANCER; SETS;

D O I：

10.1109/TNNLS.2015.2460994

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new granular self-organizing map (GSOM) is developed by integrating the concept of a fuzzy rough set with the SOM. While training the GSOM, the weights of a winning neuron and the neighborhood neurons are updated through a modified learning procedure. The neighborhood is newly defined using the fuzzy rough sets. The clusters (granules) evolved by the GSOM are presented to a decision table as its decision classes. Based on the decision table, a method of gene selection is developed. The effectiveness of the GSOM is shown in both clustering samples and developing an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data. While the superior results of the GSOM, as compared with the related clustering methods, are provided in terms of beta-index, DB-index, Dunn-index, and fuzzy rough entropy, the genes selected by the UFRFS are not only better in terms of classification accuracy and a feature evaluation index, but also statistically more significant than the related unsupervised methods. The C-codes of the GSOM and UFRFS

引用

页码：1890 / 1906

页数：17

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