Created by W.Langdon from gp-bibliography.bib Revision:1.4910
The applications of the proposed method include classification and feature processing. Classification problems play an important role in the development of knowledge engineering. Hidden relations that can be used as a basis for classification are often unclear and not easily elucidated. Thus, many machine learning algorithms have arisen to solve such problems. Feature selection and feature generation are two important techniques dealing with features. Feature selection is capable of removing useless, irrelevant, redundant, and noisy features. Feature generation generates new useful features that could improve classification accuracy.
In this study we propose a layered multi-population genetic programming method to solve classification problems. The proposed method that can complete feature selection and feature construction simultaneously is also proposed. The layered multipopulation genetic programming method employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. Each population evolves to generate a discriminant function. A set of discriminant functions generated by one layer will be integrated and be transformed by the successive layer. To improve the learning performance, an adaptive mutation probability tuning method is proposed. Moreover, a statistical-based method is proposed to solve multi-category classification problems. Several experiments on classical classification problems and real-world medical problems are conducted using different configurations. Experimental results show that the proposed methods are accurate and effective.",
312 004D:2 96-3 003639188",
Genetic Programming entries for Mick Jung-Yi Lin