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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Proceedings ArticleDOI
On the dangers of cross-validation. An experimental evaluation
R. Bharat Rao,Glenn Fung +1 more
TL;DR: It is empirically show how under such large number of models the risk for overfitting increases and the performance estimated by cross validation is no longer an effective estimate of generalization; hence, this paper provides an empirical reminder of the dangers of cross validation.
Journal ArticleDOI
Gene selection algorithm by combining reliefF and mRMR
Yi Zhang,Chris Ding,Tao Li +2 more
TL;DR: A two-stage selection algorithm by combining ReliefF and mRMR is presented: in the first stage, ReliefF is applied to find a candidate gene set; in the second stage, mR MR method is applications to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set.
MonographDOI
Data Mining: A Heuristic Approach
TL;DR: Data Mining: A Heuristic Approach is a repository for the applications of these techniques in the area of data mining.
Journal ArticleDOI
Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
TL;DR: In this paper, the authors explored the potential of a random forest regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration, which can be used as a feature selection and regression method to analyse the spectral data.
Proceedings ArticleDOI
Stable feature selection via dense feature groups
TL;DR: This work proposes a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results, and identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection.
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.