Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems
Haofan Zhang, Sheng-Uei Guan, Mengjun Xu · International Journal of Machine Learning and Computing · 2012
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.