Generating Effective Classifiers with Supervised Learning of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Chien:2003:DaWaK,

• author = "Been-Chian Chien and Jui-Hsiang Yang and Wen-Yang Lin",
• title = "Generating Effective Classifiers with Supervised Learning of Genetic Programming",
• booktitle = "Data Warehousing and Knowledge Discovery: 5th International Conference, DaWaK 2003",
• year = "2003",
• volume = "2737",
• series = "Lecture Notes in Computer Science",
• pages = "192--201",
• address = "Prague, Czech Republic",
• month = "3-5 " # sep,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/b11825",
• ISBN = "3-540-40807-X",
• abstract = "A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method and compare with other classifiers.",

}

Genetic Programming entries for Been-Chian Chien Jui-Hsiang Yang Wen-Yang Lin

Citations