A Novel GA-Taguchi-Based Feature Selection Method
This work presents a novel GA-Taguchi-based feature selection method. Genetic algorithms are utilized with randomness for “global search” of the entire search space of the intractable search problem. Various genetic operations, including crossover, mutation, selection and replacement are performed to assist the search procedure in escaping from sub-optimal solutions. In each iteration in the proposed nature-inspired method, the Taguchi methods are employed for “local search” of the entire search space and thus can help explore better feature subsets for next iteration. The two-level orthogonal array is utilized for a well-organized and balanced comparison of two levels for features—a feature is or is not selected for pattern classification—and interactions among features. The signal-to-noise ratio (SNR) is then used to determine the robustness of the features. As a result, feature subset evaluation efforts can be significantly reduced and a superior feature subset with high classification performance can be obtained. Experiments are performed on different application domains to demonstrate the performance of the proposed nature-inspired method. The proposed hybrid GA-Taguchi-based approach, with wrapper nature, yields superior performance and improves classification accuracy in pattern classification.
KeywordsGenetic Algorithm Taguchi Method Orthogonal Array Feature Subset Selection Pattern Classification
Unable to display preview. Download preview PDF.
- 5.Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
- 6.Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 2, 232–256 (1997)Google Scholar
- 7.Doak, J.: An Evaluation of Feature Selection Methods and Their Application to Computer Security. Technical Report, Univ. of California at Davis, Dept. Computer Science (1992)Google Scholar
- 10.Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD Dissertation, University of Waikato (1998)Google Scholar
- 11.Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 13.John, G.H., Kohavi, R., Pfleger, K.: Irrelevant Feature and the Subset Selection Problem. In: Proc. 11th Int’l Conf. Machine Learning, pp. 121–129 (1994)Google Scholar
- 16.Liu, H., Setiono, R.: A Probabilistic Approach to Feature Selection - A Filter Solution. In: Proc. of 13th International Conference on Machine Learning, pp. 319–327 (1996)Google Scholar
- 21.Wu, Y., Wu, A., Taguchi, G.: Taguchi Methods for Robust Design. ASME, New York (2000)Google Scholar