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
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Nature-Inspired Algorithms and Applied Optimization
TL;DR: This work intends to analyze nature-inspired algorithms both qualitatively and quantitatively, and briefly outline the links between self-organization and algorithms, and then analyze algorithms using Markov chain theory, dynamic system and other methods.
Journal ArticleDOI
Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method
TL;DR: It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.
Journal ArticleDOI
Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets.
TL;DR: The results show that feature selection can improve accuracy, specificity and sensitivity of classifiers as well as other models on Wisconsin breast cancer datasets.
Journal ArticleDOI
Privacy-preserving data mining: A feature set partitioning approach
TL;DR: The proposed data mining privacy by decomposition (DMPD) algorithm uses a genetic algorithm to search for optimal feature set partitioning and suggests that DMPD performs better than existing k-anonymity-based algorithms and there is no necessity for applying domain dependent knowledge.
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Data mining for case-based reasoning in high-dimensional biological domains
Niloofar Arshadi,Igor Jurisica +1 more
TL;DR: This work proposes the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression that achieves higher prediction accuracy and leads to the selection of a subset of features that have meaningful relationships with their class labels.
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.