Selection of Informative Inputs Using Genetic Algorithms
Modeling of processes with many input variables requires selection of informative inputs in order to construct less complex models with good generalization abilities. In this paper two feature selection methods are compared: mutual information (MI) based feature selection and genetic algorithm (GA) based feature selection. As a modeling structure a hybrid linear-neural model is used. The methods are applied to a case study: modeling of an industrial antibiotic fermentation process. It is shown that both feature selection methods can lead to similar results. 8s based feature selection can be applied to problems where only few data exist and MI can not be calculated. In GA based feature selection it is possibile to adjust the objective function in order to control the propperties of the method.
KeywordsGenetic Algorithm Feature Selection Mutual Information Root Mean Square Feature Selection Method
Unable to display preview. Download preview PDF.
- W. S. Sarle, “How to measure importance of inputs?,” 1997. URI = ftp://ftp.sas.com/pub/neural/importance.html.
- B. Bonnlander, “Nonparametric selection of input variables for connectionist learning”, PhD thesis, University of Colorado, 1996.Google Scholar
- P. Potočnik and I. Grabec, “Neural-genetic system for modeling of antibiotic fermentation process,” in Proceedings of the International ICSC Symposium on Engineering of Intelligent Systems EIS′98, Volume 2, (Tenerife, Spain), pp. 307–313, 1998.Google Scholar
- P. Potočnik, “Nonparametric modeling of a fermentation process,” Master’s thesis, University of Ljubljana, Faculty of Mechanical Engineering, Ljubljana, 1997. (in Slovenian).Google Scholar
- M. J. Willis, C. D. Massimo, G. A. Montague, M. T. Tham, and A. J. Morris, “Artificial neural networks in process engineering,” IEE Proceedings–D, vol. 138, no. 3, pp. 256–266, 1990.Google Scholar