Feature selection and classification model construction on type 2 diabetic patients' data
Artificial Intelligence in Medicine
Software fault localization using feature selection
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Using kNN model for automatic feature selection
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Exploiting randomness for feature selection in multinomial logit: a CRM cross-sell application
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Self-organizing maps for translating health care knowledge: a case study in diabetes management
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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ReliefF is a feature mining technique, which has been successfully used in data mining applications.However, ReliefF is sensitive to the definition of relevance that is used in its implementation and when handling a large data set, it is computationally expensive.This paper presents an optimisation (Feature Selection via Supervised Model Construction) for data transformation and starter selection, and evaluates its effectiveness with C4.5.Experiments indicate that the proposed method gave improvement of computation efficiency whilst maintaining classification accuracy of trial data sets.