期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2018
卷号：8
期号：4
页码：2338-2350
DOI：10.11591/ijece.v8i4.pp2338-2350
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. This paper proposed an enhanced firefly model based on firefly algorithm to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposed model is to improve the classification accuracy by using the selected features ireflies produced from this model, and in turn reduce classification errors. Modeling firefly in this research appears through simulating firefly position by cell chi-square value which is changed after every move, and simulating firefly intensity by calculating a set of different fitness functions as a weight for feature. K-nearest neighbor and Discriminant analysis are used as classifiers to test the proposed firefly model in selecting features. Experimental results showed that the proposed model that combines firefly algorithm with chi-square and different fitness functions can provide better results than others. Results showed that reduction of dataset is useful for gaining higher accuracy in classification.
其他摘要：Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. This paper proposed an enhanced firefly model based on firefly algorithm to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposed model is to improve the classification accuracy by using the selected features ireflies produced from this model, and in turn reduce classification errors. Modeling firefly in this research appears through simulating firefly position by cell chi-square value which is changed after every move, and simulating firefly intensity by calculating a set of different fitness functions as a weight for feature. K-nearest neighbor and Discriminant analysis are used as classifiers to test the proposed firefly model in selecting features. Experimental results showed that the proposed model that combines firefly algorithm with chi-square and different fitness functions can provide better results than others. Results showed that reduction of dataset is useful for gaining higher accuracy in classification.
