Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1129
Title: An incremental approach to MSE-based feature selection
Authors: Guan, SU
Qi, Y
Bao, C
Keywords: Feature selection;Classifier;Neural network;Feedforward neural network;Minimum squared error (MSE);Incremental training;Input attribute
Issue Date: 2007
Publisher: World Scientific
Citation: International Journal of Computational Intelligence and Applications. In press
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 a Minimum Squared Error (MSE) based feature ranking scheme, four 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 methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost.
URI: http://bura.brunel.ac.uk/handle/2438/1129
ISSN: 1469-0268
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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