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
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Comprehensible Interpretation of Relief's Estimates
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
A selective sampling approach to active feature selection
Artificial Intelligence
Data mining in bioinformatics using Weka
Bioinformatics
Exploiting expert knowledge in genetic programming for genome-wide genetic analysis
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Ant Colony Optimization for Genome-Wide Genetic Analysis
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Implementing ReliefF filters to extract meaningful features from genetic lifetime datasets
Journal of Biomedical Informatics
An analysis of new expert knowledge scaling methods for biologically inspired computing
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Improving multi-relief for detecting specificity residues from multiple sequence alignments
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Evolutionary feature selection for classification: a plug-in hybrid vehicle adoption application
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An adaption of relief for redundant feature elimination
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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An important goal of human genetics is the identification of DNA sequence variations that are predictive of who is at risk for various common diseases. The focus of the present study is on the challenge of detecting and characterizing nonlinear attribute interactions or dependencies in the context of a genome-wide genetic study. The first question we address is whether the ReliefF algorithm is suitable for attribute selection in this domain. The second question we address is whether we can improve ReliefF for selecting important genetic attributes. Using simulated genetic datasets, we show that ReliefF is significantly better than a naïve chi-square test of independence for selecting two interacting attributes out of 103 candidates. In addition, we show that ReliefF can be improved in this domain by systematically removing the worst attributes and re-estimating ReliefF weights. Our simulation studies demonstrate that this new Tuned ReliefF (TuRF) algorithm is significantly better than ReliefF.