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
Feature selection and blind source separation in an EEG-based brain-computer interface
TL;DR: It is hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct "yes"/"no" BCI from a single session and the results suggest that BSS and feature selection can be use to improve the performance of even a "direct," single-session BCI.
Proceedings ArticleDOI
Efficiently handling feature redundancy in high-dimensional data
TL;DR: This paper provides a study of feature redundancy in high-dimensional data and proposes a novel correlation-based approach to feature selection within the filter model that is efficient and effective in removing redundant and irrelevant features.
Journal ArticleDOI
Feature Selection of Gene Expression Data for Cancer Classification: A Review
TL;DR: A review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification is presented.
Journal ArticleDOI
Feature subset selection using a new definition of classifiability
Ming Dong,Ravi Kothari +1 more
TL;DR: The proposed approach to subset selection based on a novel definition of the classifiability of a given data characterizes the relative ease with which some labeled data can be classified and is at least as good as that obtained with the wrapper approach.
Journal ArticleDOI
Space Structure and Clustering of Categorical Data
TL;DR: A novel data-representation scheme for the categorical data, which maps a set of categorical objects into a Euclidean space is designed and a general framework for space structure based categorical clustering algorithms (SBC) is designed.
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.