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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Subspace based feature selection for pattern recognition
Serkan Gunal,Rifat Edizkan +1 more
TL;DR: This paper proposes subspace based separability measures to determine the individual discriminatory power of the features and these measures are then employed to sort and select features in a multi-class manner.
Journal ArticleDOI
A hybrid feature selection method for DNA microarray data
TL;DR: In this paper, correlation-based feature selection and the Taguchi-genetic algorithm and the TGA method were combined into a hybrid method, and the K-nearest neighbor with the leave-one-out cross-validation method served as a classifier for eleven classification profiles to calculate the classification accuracy.
Journal ArticleDOI
CovSel: Variable selection for highly multivariate and multi-response calibration: Application to IR spectroscopy
TL;DR: In this paper, a new variable selection method, CovSel, is proposed for multi-response NIR spectroscopy, where variable selection is performed step by step on the basis of their global covariance with all the responses.
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Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data
TL;DR: The results show the usefulness of random forest‐ and SVM‐based feature selection approaches in comparison to the SVM/GA approach for land cover classification problems with hyperspectral data, and an improved performance using these techniques in relation to the maximum noise transformation based feature extraction technique.
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A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets
Jin-Seo Park,Se-Jong Oh +1 more
TL;DR: A new concave hull algorithm for n-dimensional datasets is proposed, simple but creative, and its application to dataset analysis is shown.
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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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.