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Open AccessJournal ArticleDOI

Wrappers for feature subset selection

Ron Kohavi, +1 more
- 01 Dec 1997 - 
- Vol. 97, Iss: 1, pp 273-324
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.
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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.

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Citations
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Journal ArticleDOI

Changes in exon–intron structure during vertebrate evolution affect the splicing pattern of exons

TL;DR: By calculating evolutionary rate shifts in exons, this work identified cis-acting regulatory sequences that became fixed during the transition from early vertebrates to mammals and revealed many features of exons that can discriminate alternative from constitutive exons.
Proceedings Article

Efficient learning of selective Bayesian network classifiers

TL;DR: Experimental results show the resulting classifiers are competitive with (or superior to) the best classifiers, based on both Bayesian networks and other formalisms, and that the computational time for learning and using these classifiers is relatively small.
Journal ArticleDOI

Multi-objective evolutionary feature selection for online sales forecasting

TL;DR: This work builds an accurate regression model for online sales forecasting obtained via a novel feature selection methodology composed by the application of the multi-objective evolutionary algorithm ENORA as search strategy in a wrapper method driven by the well-known regression model learner Random Forest.
Journal ArticleDOI

A new hybrid approach for intrusion detection using machine learning methods

TL;DR: It has been shown that the proposedIDS has high accuracy and a low false positive rates in all attack types.
Journal ArticleDOI

DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

TL;DR: DWFS is a web-based tool that allows for efficient selection of features for a variety of problems and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book

C4.5: Programs for Machine Learning

TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book

Applied Regression Analysis

TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI

Induction of Decision Trees

J. R. Quinlan
- 25 Mar 1986 - 
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.