Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TLDR
The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.About:
This article is published in Artificial Intelligence.The article was published on 1997-12-01 and is currently open access. It has received 8610 citations till now. The article focuses on the topics: Feature selection & Minimum redundancy feature selection.read more
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Data Mining: Concepts and Techniques
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Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Journal ArticleDOI
Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.
References
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Journal ArticleDOI
On automatic feature selection
W. Siedlecki,Jack Sklansky +1 more
TL;DR: In this paper, a review of feature selection for multidimensional pattern classification is presented, and the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms are compared.
Proceedings Article
Feature subset selection using the wrapper method: overfltting and dynamic search space topology
Ron Kohavi,Dan Sommerfield +1 more
TL;DR: This work introduces compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets and shows that compound operators unify previous approaches that deal with relevant and irrelevant features.
Journal ArticleDOI
Selecting a classification method by cross-validation
TL;DR: Empirically, cross-validation may lead to higher average performance than application of any single classification strategy, and it also cuts the risk of poor performance.
Proceedings Article
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Oded Maron,Andrew W. Moore +1 more
TL;DR: This paper focuses on the special case of leave-one-out cross validation applied to memory-based learning algorithms, but it is argued that it is applicable to any class of model selection problems.
Proceedings Article
Solving time-dependent planning problems
Mark S. Boddy,Thomas Dean +1 more
TL;DR: This paper analyzes and solves a moderately complex time-dependent planning problem involving path planning for a mobile robot, as a way of exploring a methodology for applying expectation-driven iterative refinement.