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IJON
2006

Feature selection and classification using flexible neural tree

8 years 7 months ago
Feature selection and classification using flexible neural tree
The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate. r 2006 Elsevier B.V. All rights reserved.
Yuehui Chen, Ajith Abraham, Bo Yang
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where IJON
Authors Yuehui Chen, Ajith Abraham, Bo Yang
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