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Ordered incremental training with genetic algorithms
Author(s) -
Zhu Fangming,
Guan ShengUei
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20046
Subject(s) - computer science , classifier (uml) , machine learning , benchmark (surveying) , artificial intelligence , incremental learning , data mining , genetic algorithm , training set , algorithm , geodesy , geography
Abstract Incremental training has been used for genetic algorithm (GA)‐based classifiers in a dynamic environment where training samples or new attributes/classes become available over time. In this article, ordered incremental genetic algorithms (OIGAs) are proposed to address the incremental training of input attributes for classifiers. Rather than learning input attributes in batch as with normal GAs, OIGAs learn input attributes one after another. The resulting classification rule sets are also evolved incrementally to accommodate the new attributes. Furthermore, attributes are arranged in different orders by evaluating their individual discriminating ability. By experimenting with different attribute orders, different approaches of OIGAs are evaluated using four benchmark classification data sets. Their performance is also compared with normal GAs. The simulation results show that OIGAs can achieve generally better performance than normal GAs. The order of attributes does have an effect on the final classifier performance where OIGA training with a descending order of attributes performs the best. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1239–1256, 2004.