Abstract
This paper proposes a method of identifying features which are important for each class. This entails selecting the features specifically for each class. This is carried out by using the simulated annealing technique. The algorithm is run separately for each class resulting in the feature subset for that class. A test pattern is classified by running a classifier for each class and combining the result. The 1NN classifier is the classification algorithm used. Results have been reported on eight benchmark datasets from the UCI repository. The selected features, besides giving good classification accuracy, gives an idea of the important features for each class.
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Devi, V.S. (2015). Class Specific Feature Selection Using Simulated Annealing. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_2
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DOI: https://doi.org/10.1007/978-3-319-26832-3_2
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