DocumentCode :
2773351
Title :
An Evolutionary Approach to Data Classification - Hybrid Real-Coded Genetic Algorithm with Pruning
Author :
Zhang, Hong ; Ishikawa, Masumi
Author_Institution :
Kyutech Inst. of Technol., Kitakyushu
fYear :
0
fDate :
0-0 0
Firstpage :
2926
Lastpage :
2931
Abstract :
We have already proposed a hybrid real-coded genetic algorithm with local search (HRGA/LS) for improving the search performance of a conventional real-coded genetic algorithm. To further enhance the generalization ability of classification models by HRGA/LS, this paper proposes a hybrid real-coded genetic algorithm with pruning (HRGA/P). A crucial idea here is the introduction of a regularizer into a fitness function for better generalization. Accordingly, the proposed algorithm has the following advantages: 1) finding near optimal classification models efficiently by a hybrid technique, 2) improving the generalization ability of classification models by a regularization technique. Applications of the proposed algorithm to an iris classification problem well demonstrate its effectiveness. Our experimental results clearly indicate that HRGA/P has higher classification performance not only in training data but also in test data (classification rate: 96.6%) than the conventional algorithms such as backpropagation (classification rate: 94.1%) and structural learning with forgetting (classification rate: 95.0%).
Keywords :
generalisation (artificial intelligence); genetic algorithms; pattern classification; search problems; data classification; evolutionary approach; generalization; hybrid real-coded genetic algorithm; local search; pruning; Backpropagation algorithms; Data analysis; Genetic algorithms; Iris; Knowledge acquisition; Modeling; Pattern recognition; Systems engineering and theory; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.247225
Filename :
1716495
Link To Document :
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