Abstract
This paper introduces a new dynamic feature selection to classification algorithms, which is based on individual similarity and it uses a clustering algorithm to select the best features for an instance individually. In addition, an empirical analysis will be performed to evaluate the performance of the proposed method and to compare it with existing feature selection methods, applying to classification problems. The results shown in this paper indicate that the proposed method had better performance results than the existing methods compared, in most cases.
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Dantas, C.A., Nunes, R.O., Canuto, A.M.P., Xavier-Júnior, J.C. (2017). Dynamic Feature Selection Based on Clustering Algorithm and Individual Similarity. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_53
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DOI: https://doi.org/10.1007/978-3-319-68612-7_53
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