- Toward Optimal Feature Selection Daphne Koller
- Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
- Comparison of feature ranking methods based on information entropy.
- 1 FEATURE WEIGHTING FOR LAZY LEARNING ALGORITHMS
- Tutorial for InfoSel++: Information Based Feature Selection C++ Library
- Department of Computer Science Hamilton, NewZealand
- From Proceedings of the AAAI Fall Symposium on Relevance (1994). New Orleans, LA: AAAI Press. Selection of Relevant Features in Machine Learning
- Comparison of feature ranking methods based on information entropy.
- Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter
- FEATURE RANKING METHODS BASED ON INFORMATION ENTROPY WITH PARZEN WINDOWS
- FEATURE SELECTION BASED ON INFORMATION THEORY, CONSISTENCY AND SEPARABILITY INDICES.
- Feature Selection for High-Dimensional Data: A Pearson Redundancy Based Filter
- Comparison of Various Feature Selection Methods in Application to Prototype Best
- Information Theory vs Correlation Base Feature Ranking Methods in Application to Solving
- Learning With Many Irrelevant Features Hussein Almuallim and Thomas G. Dietterich
- To appear in IEEE Expert Spec. Issue on Feature Transformation and Subset Selection
- A Kolmogorov-Smirnov Correlation-Based Filter for Microarray Data
- Feature Selection and Ranking Filters. Wlodzislaw Duch1,2, Tomasz Winiarski1, Jacek Biesiada3, and Adam Kachel3
- A molecular dynamics study of fullerenecarbon monoxide mixture S. Paluchaa,b,*, Z. Gburskib
- Superconductivity in the pair-tunneling model close to the metal-insulator transition.
- Journal of Superconductivity: Incorporating Novel Magnetism, Vol. 13, No. 3, 2000 Phonon-Induced Superconductivity and the Staggered
- Feature Selection Based on Information Theory Wlodzislaw Duch1, Jacek Biesiada2, Tomasz Winiarski1, Karol Grudzinski1, and
- Infosel++: Information Based Feature Selection C++ , J. Biesiada1,3
- Feature Selection and Ranking Filters Wlodzislaw Duch
- Reprinted with corrections from The Bell System Technical Journal, Vol. 27, pp. 379423, 623656, July, October, 1948.
- Feature Selection for Supervised Classification: A Kolmogorov-Smirnov Class Correlation-Based Filter
- 1. APPLICATION OF OPTIMISATION TECHNIQUES IN INDUCTION HEATERS DESIGN
- A Probabilistic Approach to Feature Selection --A Filter Solution
- Using Decision Trees to Improve Case-Based Learning Claire Cardie
