This paper considers feature selection in a multiclass classification scenario where the goal is to determine a subset of available features which is most discriminative and informative for all the classes simultaneously. Based on the data distributions of classes in the feature space, this paper first presents a model selection criterion named multiclass kernel polarization (MKP) to evaluate the goodness of a kernel in multiclass classification scenario, and then optimizes the scale factors assigned to each feature in a kernel by maximizing this criterion to identify the more relevant features. The proposed method is demonstrated with two UCI machine learning benchmark examples. © 2012 IEEE.
CITATION STYLE
Wang, T., & Xu, S. (2012). Multiclass feature selection via kernel parameter optimization. In Proceedings - 2012 5th International Conference on Intelligent Computation Technology and Automation, ICICTA 2012 (pp. 213–216). https://doi.org/10.1109/ICICTA.2012.60
Mendeley helps you to discover research relevant for your work.