- author = "Jung-Yi Lin and Been-Chian Chien and Tzung-Pei Hong",
- title = "A Function-Based Classifier Learning Scheme Using Genetic Programming",
- booktitle = "Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, PAKDD 2002",
- editor = "M.-S. Chen and P. S. Yu and B. Liu",
- year = "2002",
- volume = "2336",
- pages = "92--103",
- series = "Lecture Notes in Computer Science",
- address = "Taipel, Taiwan",
- publisher_address = "Heidelberg",
- month = "6-8 " # may,
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-540-43704-8",
- CODEN = "LNCSD9",
- ISSN = "0302-9743",
- bibdate = "Tue Sep 10 19:09:39 MDT 2002",
-
DOI = "
doi:10.1007/3-540-47887-6_9",
- acknowledgement = ack-nhfb,
- size = "12 pages",
- abstract = "Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multi-category classifiers based on genetic programming. For a $k$-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate $k$ discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a $Z$-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.",
-
notes = "http://arbor.ee.ntu.edu.tw/pakdd02/
chinese version http://www.bohr.idv.tw/chinese/pdf/B013.pdf",
