doi: 10.7763/IJMLC.2012.V2.241
Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems
Haofan Zhang1
,
Sheng-Uei Guan2 ,
Mengjun Xu2
- 1University of Waterloo, Canada.
- 2Computer Science and Software Engineering Department at Xi’an Jiaotong-Liverpool University, Suzhou, China.
Abstract
Rule-based classifiers trained by Genetic Algorithms (GAs) have been one of the most prevailing solutions for pattern classification problem. This paper introduces an algorithm named Recursive Learning of Genetic Algorithms featuring Incremental Attribute Learning (RLGA-IAL) developed from the Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set (RLGA). Instead of training all the attributes in a batch, RLGA-IAL integrates the attributes sequentially. By projecting a large multidimensional search space to single-dimensional space spaces with integration, it reduces the difficulty in deriving the classification rules.Though a series of experiments, RLGA-IAL shows a successful and promising performance in classification problems with the dimension of the datasets ranging from 5 to 60.
Keywords
- Genetic algorithm
- high dimensional classification
- incremental attribute learning
- recursive learning
How to Cite
Haofan Zhang, Sheng-Uei Guan, and Mengjun Xu, "Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems," International Journal of Machine Learning, vol. 2, no. 6, pp. 802-806, 2012. https://doi.org/10.7763/IJMLC.2012.V2.241
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).