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
Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients
TL;DR: An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed, which can correctly select the discriminating input features and also achieve high classification accuracy.
Journal ArticleDOI
Differential evolution for filter feature selection based on information theory and feature ranking
TL;DR: The results show that the proposed criterion outperforms MIFS in both single objective and multi-objective DE frameworks, and indicates that considering feature selection as a multi- objective problem can generally provide better performance in terms of the feature subset size and the classification accuracy.
Journal ArticleDOI
Knowledge derived from wikipedia for computing semantic relatedness
TL;DR: Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and it is shown that Wikipedia outperforms WordNet on some datasets.
Journal ArticleDOI
Recent advances and emerging challenges of feature selection in the context of big data
TL;DR: The origins and importance of feature selection are discussed and recent contributions in a range of applications are outlined, from DNA microarray analysis to face recognition.
Proceedings Article
Text Classification Using WordNet Hypernyms
Sam Scott,Stan Matwin +1 more
TL;DR: Experiments show that for some of the more difficult tasks the hypernym density representation leads to significantly more accurate and more comprehensible rules.
References
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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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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.