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
Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM)
TL;DR: The work presented in this paper involves building an effective fault prediction tool by identifying and investigating the predictive power of several well-known and widely used software metrics for fault prediction by using Least Squares Support Vector Machine learning method associated with linear, polynomial and radial basis function kernel functions.
Proceedings ArticleDOI
Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
TL;DR: A novel approach to constructing a good ensemble of classifiers using rough set theory and database operations, where each reduct is a minimum subset of attributes and has the same classification ability as the entire attributes.
Journal ArticleDOI
Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques
TL;DR: Experiments show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules.
Journal ArticleDOI
Text classification using genetic algorithm oriented latent semantic features
Alper Kursat Uysal,Serkan Gunal +1 more
TL;DR: Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.
Journal ArticleDOI
Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks
TL;DR: The use of genetic algorithms is explored to automatically select the relevant input variables for classification trees and artificial neural networks, predicting the presence or absence of benthic macroinvertebrate taxa in Flanders, Belgium.
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.