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
Citations
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Journal ArticleDOI
Feature selection for IoT based on maximal information coefficient
TL;DR: The results show that the proposed method achieves better performance than the comparison methods, markedly reducing feature dimensionality in order to process the tremendous quantities of data in IoT.
Using Information Gain Attribute Evaluation to Classify Sonar Targets
TL;DR: In the authors' experiments, IG attribute evaluation significantly improves C4.5 decision tree and shows that feature selection helps increase computational efficiency while improving classification accuracy.
Journal ArticleDOI
Large Margin Feature Weighting Method via Linear Programming
TL;DR: This paper considers feature selection method for multimodally distributed data, and presents a large margin feature weighting method for k-nearest neighbor (kNN) classifiers, which aims at separating different classes by large local margins and pulling closer together points from the same class.
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Fuzzy criteria for feature selection
TL;DR: This paper proposes to use fuzzy criteria in feature selection by using a fuzzy decision making framework, which allows for a more flexible definition of the goals infeature selection, and avoids the problem of weighting different goals is classical multi-objective optimization.
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Using cooperative game theory to optimize the feature selection problem
TL;DR: The framework first introduces a cooperative game theoretic method based on Shapley value to evaluate the weight of each feature according to its influence to the intricate and intrinsic interrelation among features, and provides the weighted features to feature selection algorithm.
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.
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Classification and Regression Trees.
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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.
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Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
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
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.