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Incremental Hyperplane Partitioning for Classification

Incremental Hyperplane Partitioning for Classification

Tao Yang, Sheng-Uei Guan, Jinghao Song, Binge Zheng, Mengying Cao, Tianlin Yu
Copyright: © 2013 |Volume: 4 |Issue: 2 |Pages: 13
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466632769|DOI: 10.4018/jaec.2013040106
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MLA

Yang, Tao, et al. "Incremental Hyperplane Partitioning for Classification." IJAEC vol.4, no.2 2013: pp.67-79. http://doi.org/10.4018/jaec.2013040106

APA

Yang, T., Guan, S., Song, J., Zheng, B., Cao, M., & Yu, T. (2013). Incremental Hyperplane Partitioning for Classification. International Journal of Applied Evolutionary Computation (IJAEC), 4(2), 67-79. http://doi.org/10.4018/jaec.2013040106

Chicago

Yang, Tao, et al. "Incremental Hyperplane Partitioning for Classification," International Journal of Applied Evolutionary Computation (IJAEC) 4, no.2: 67-79. http://doi.org/10.4018/jaec.2013040106

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Abstract

The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

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