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
Data-driven dynamic emulation modelling for the optimal management of environmental systems
TL;DR: Preliminary results show that the proposed approach significantly simplifies the learning of good operating policies and can highlight interesting properties of the system to be controlled.
Journal ArticleDOI
A feature subset selection algorithm automatic recommendation method
TL;DR: A meta learning based FSS algorithm automatic recommendation method that ranks all the candidate FSS algorithms according to their performance on similar data sets, and chooses the algorithms with best performance as the appropriate ones.
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A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection
TL;DR: A novel mutation enhanced BPSO-SVM algorithm is presented by adjusting the memory of local and global optimum (LGO) and increasing the particles’ mutation probability for feature selection to overcome convergence premature problem and achieve high quality features.
Journal ArticleDOI
Channel selection for automatic seizure detection
Jonas Duun-Henriksen,Jonas Duun-Henriksen,Troels W. Kjaer,Rasmus Elsborg Madsen,Line Sofie Remvig,Carsten E. Thomsen,Helge Bjarup Dissing Sørensen +6 more
TL;DR: Using only three EEG channels, a seizure detection sensitivity of 96% and a false detection rate of 0.14/h were obtained, a 4% improvement in sensitivity compared to seizure detection using channels recorded directly on the epileptic focus.
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Variable Selection Methods for Model-based Clustering
TL;DR: This review provides a summary of the methods developed for variable selection in model-based clustering and existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.
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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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.
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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.