Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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The increasing amount and complexity of data used in predictive toxicology calls for efficient and effective feature selection methods in data pre-processing for data mining. In this paper, we propose a kNN model-based feature selection method (kNNMFS) aimed at overcoming the weaknesses of ReliefF method. It modifies 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. The performance of kNNMFS was evaluated on a toxicity data set Phenols using a linear regression algorithm. Experimental results indicate that kNNMFS has a significant improvement in the classification accuracy for the trial data set.