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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Book
Network Anomaly Detection: A Machine Learning Perspective
TL;DR: Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks.
Proceedings Article
From Transformation-Based Dimensionality Reduction to Feature Selection
TL;DR: This paper introduces a general approach for converting transformation-based methods to feature selection methods through l1/l∞ regularization and illustrates how this approach can be utilized to convert linear discriminant analysis and the dimensionality reduction version of the Hilbert-Schmidt Independence Criterion to two new feature selection algorithms.
Book ChapterDOI
An evaluation of filter and wrapper methods for feature selection in categorical clustering
TL;DR: Results confirm the utility of feature selection for clustering and the theoretical superiority of wrapper methods and suggest evidence that filters are a reasonably alternative with limited computational cost.
Proceedings ArticleDOI
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
TL;DR: A novel mechanism to automatically find those features which most improve the quality of the machine learned heuristic, which is able to achieve 76% of the maximum available speedup, outperforming existing approaches.
Journal ArticleDOI
Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data
Pradipta Maji,Sushmita Paul +1 more
TL;DR: A new feature selection algorithm is presented based on rough set theory that selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes.
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.
Book
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.