Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/56034
Title: Feature selection for modular networks based on incremental training
Authors: Guan, S.-U. 
Liu, J.
Keywords: Classifier
Feature selection
Feedforward neural network
Incremental training
Input attribute
Neural network
Issue Date: 2005
Source: Guan, S.-U.,Liu, J. (2005). Feature selection for modular networks based on incremental training. Journal of Intelligent Systems 14 (4) : 353-383. ScholarBank@NUS Repository.
Abstract: Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with an FLD feature ranking scheme, three batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature-selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection method works well on several benchmark problems. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost.
Source Title: Journal of Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/56034
ISSN: 03341860
Appears in Collections:Staff Publications

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