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.
Index Terms—Genetic algorithm, high dimensional classification, incremental attribute learning, recursive learning.
Haofan Zhang is with University of Waterloo, Canada (e-mail: email@example.com).
Sheng-Uei Guan is with the Computer Science and Software Engineering Department at Xi’an Jiaotong-Liverpool University, Suzhou, China (e-mail: firstname.lastname@example.org).
Mengjun Xu is with Xi’an Jiaotong-Liverpool University, Suzhou, China (e-mail: email@example.com).
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 and Computing vol.2, no. 6, pp. 802-806, 2012.