Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Modeling timbre distance with temporal statistics from polyphonic music
IEEE Transactions on Audio, Speech, and Language Processing
Bootstrap feature selection for ensemble classifiers
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Journal of Machine Learning Research
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In addition to accuracy, stability is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure, and perform both accuracy and stability measurements of MRMR (Minimum Redundancy Maximum Relevance) feature selection algorithm on different data sets. The two feature evaluation criteria used by MRMR, MID (Mutual Information Difference) and MIQ (Mutual Information Quotient), result in similar accuracies, but MID is more stable. We also introduce a new feature selection criterion, MID *** , where redundancy and relevance of selected features are controlled by parameter *** .