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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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
The B* tree search algorithm: a best-first proof procedure
TL;DR: The algorithm, which is named B*, finds a proof that an arc at the root of a search tree is better than any other by attempting to find both the best arc atThe root and the simplest proof, in best-first fashion.
Book ChapterDOI
Searching for Dependencies in Bayesian Classifiers
TL;DR: It is shown that the backward sequential elimination and joining algorithm provides the most improvement over the naive Bayesian classifier and that the violations of the independence assumption that affect the accuracy of the classifier can be detected from training data.
Book ChapterDOI
Feature Selection Using Rough Sets Theory
TL;DR: The paper is related to one of the aspects of learning from examples, namely learning how to identify a class of objects a given object instance belongs to, and a method of generating sequence of features allowing such identification is presented.
Journal ArticleDOI
Stochastic discrete optimization
TL;DR: In this paper, a stochastic search method is proposed for finding a global solution to the discrete optimization problem in which the objective function must be estimated by Monte Carlo simulation, and it is shown under mild conditions that the Markov chain is strongly ergodic.
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Essentials of Artificial Intelligence
TL;DR: Based on the author's course at Stanford University, Essentials of Artificial Intelligence is an integrated, cohesive introduction to the field that combines clear presentations with humor and AI anecdotes.