Browsing by Author "Lin, Jung-Yi"

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Classifier design with feature selection and feature extraction using layered genetic programming
(2008-02-01)
Lin, Jung-Yi
;
Ke, Hao-Ren
;
Chien, Been-Chian
;
Yang, Wei-Pang
;
資訊工程學系
This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP. (C) 2007 Elsevier Ltd. All rights reserved.
Designing a classifier by a layered multi-population genetic programming approach
(2007-08-01)
Lin, Jung-Yi
;
Ke, Hao-Ren
;
Chien, Been-Chian
;
Yang, Wei-Pang
;
資訊工程學系
This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.