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 Article
Exact top-k feature selection via l 2,0 -norm constraint
Xiao Cai,Feiping Nie,Heng Huang +2 more
TL;DR: Although the proposed model is not a convex problem, it outperforms the approximate convex counterparts and state-of-art feature selection methods evaluated in terms of classification accuracy by two popular classifiers.
Journal ArticleDOI
Feature Subset Selection and Ranking for Data Dimensionality Reduction
Journal ArticleDOI
The ALAMO approach to machine learning
TL;DR: ALAMO's constrained regression methodology is used to further refine concentration models, resulting in models that perform better on validation data and satisfy upper and lower bounds placed on model outputs.
Journal ArticleDOI
Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods
D.A. Saldana,Laurie Starck,Pascal Mougin,Bernard Rousseau,Ludivine Pidol,Nicolas Jeuland,Benoit Creton +6 more
TL;DR: In this article, the authors report the development of models for the prediction of two fuel properties: flash points (FPs) and cetane numbers (CNs), using quantitative structure property relationship (QSPR) approaches.
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Accelerating wrapper-based feature selection with K-nearest-neighbor
TL;DR: Both the experimental results and theoretical analysis demonstrated that the proposed approach markedly accelerates the wrapper-based feature selection process without degrading the high classification accuracy, and the space complexity analysis indicated that the additional space overhead is affordable in practice.
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.
Journal ArticleDOI
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.
Book
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.