Please use this identifier to cite or link to this item:
Title: Hierarchical incremental class learning with reduced pattern training
Authors: Guan, SU
Bao, C
Sun, RT
Keywords: Classifier systems;Output parallelism;Instance selection
Issue Date: 2006
Publisher: Springer
Citation: Neural Processing Letters 24(2): 163-177, Sep 2006
Abstract: Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement. This paper proposes a theoretical model to evaluate the performance of HICL and presents an approach to improve the classification accuracy of HICL by applying the concept of Reduced Pattern Training (RPT). The theoretical analysis shows that HICL can achieve better classification accuracy than Output Parallelism [1]. The procedure for RPT is described and compared with the original training procedure. RPT reduces systematically the size of the training data set based on the order of sub-networks built. The results from four benchmark classification problems show much promise for the improved model.
ISSN: 1370-4621
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf163.03 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.