A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Feature Selection via Supervised Model Construction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Active feature selection using classes
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
An effective combination of multiple classifiers for toxicity prediction
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
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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 bykNN 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.