This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications - those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation based on inductive information in each representative obtained by kNN model. We have evaluated the performance of the proposed kNN model-based feature selection method on toxicity dataset Phenols with two different endpoints. Experimental results indicate that the proposed feature selection method has a significant improvement in the classification accuracy for the trial dataset. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Guo, G., Neagu, D., & Cronin, M. T. D. (2005). Using kNN model for automatic feature selection. In Lecture Notes in Computer Science (Vol. 3686, pp. 410–419). Springer Verlag. https://doi.org/10.1007/11551188_44
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