Created by W.Langdon from gp-bibliography.bib Revision:1.7954
In this research work, endeavours have been made to address the feature extraction issue using the strength of evolutionary computation. A genetic programming based feature extraction framework is proposed for the problem. The idea is inspired by the biological evolution process, in which the stronger features survive and weaker ones are washed out. It is believed that for features under-going such evolution, the discrimination ability will have been optimised under a certain criterion. Therefore, the classification benefits from such optimisation of features.
Compared to conventional feature extraction methods, such as Principal Components Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), Kernel Principal Components Analysis (KPCA) and Generalised Discriminant Analysis (GDA), this approach undertakes a genetic search in the feature space. The objective of the search is to get closer to the core of the problem. Rather than over-fitting, the genuine characteristic of each pattern is more likely to be identified. Practically, certain termination criterion arc set as the job-done points, such as a threshold on the fitness value, the maximum number of generations, etc. They are empirical values set to achieve satisfactory results for specific applications.
In order to cover different scenarios and thoroughly examine the capability of the proposed method, three systems are designed, including a multi-feature extraction system for multiclass problems, a single-feature extraction system for dual-class problems, and a single feature extraction system for multi-class problems. Real data are used for the evaluation of the performance of the systems developed. A series of experiments are conducted to evaluate and compare the results obtained using the combination of different feature extraction methods and classification methods. The classifiers involved range from a simple Minimal Distance classifier (MDC) to a sophisticated Multi-Layer Perceptron (MLP) neural network.
The results demonstrate that the proposed approach is superior to conventional methods for feature extraction for selected applications. The genetic programming system outperforms other systems in terms of classification success. The experimental results are promising. It is believed that this design can be implemented and applied in practical pattern recognition problems as a remarkably cost-effective solution.",
Genetic Programming entries for Hong Guo