Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54522
Title: On Feature Selection and Rule Extraction for High Dimensional Data: A Case of Diffuse Large B-Cell Lymphomas Microarrays Classification
Authors: Narissara Eiamkanitchat
Nipon Theera-Umpon
Sansanee Auephanwiriyakul
Keywords: Engineering
Mathematics
Issue Date: 1-Jan-2015
Abstract: © 2015 Narissara Eiamkanitchat et al. Neurofuzzy methods capable of selecting a handful of useful features are very useful in analysis of high dimensional datasets. A neurofuzzy classification scheme that can create proper linguistic features and simultaneously select informative features for a high dimensional dataset is presented and applied to the diffuse large B-cell lymphomas (DLBCL) microarray classification problem. The classification scheme is the combination of embedded linguistic feature creation and tuning algorithm, feature selection, and rule-based classification in one neural network framework. The adjustable linguistic features are embedded in the network structure via fuzzy membership functions. The network performs the classification task on the high dimensional DLBCL microarray dataset either by the direct calculation or by the rule-based approach. The 10-fold cross validation is applied to ensure the validity of the results. Very good results from both direct calculation and logical rules are achieved. The results show that the network can select a small set of informative features in this high dimensional dataset. By a comparison to other previously proposed methods, our method yields better classification performance.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84948798462&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/54522
ISSN: 15635147
1024123X
Appears in Collections:CMUL: Journal Articles

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