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
Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction
TL;DR: Experiments showed that the proposed ensemble of probabilistic neural network (PNN) and neighborhood rough set model based gene reduction approach to tumor classification can obtain both high and stable classification performance, which is not too sensitive to the number of initially selected genes and competitive to most existing methods.
Journal ArticleDOI
Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture.
TL;DR: The proposed machine learning (ML)-based botnet attack detection framework with sequential detection architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.
Journal ArticleDOI
A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
TL;DR: The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly.
Journal ArticleDOI
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS
TL;DR: In this article, feature subset selection is applied to distinguish between the two subgroups of cirrhotic patients, and the estimated accuracies obtained tally with the results of previous studies. But, the medical significance of the subset selected by the classifiers along with the comprehensibility of Bayesian models is greatly appreciated by physicians.
Journal ArticleDOI
Towards a Memetic Feature Selection Paradigm [Application Notes]
Zexuan Zhu,Sen Jia,Zhen Ji +2 more
TL;DR: This article considers two real-world feature selection applications: gene selection in cancer classification based on microarray data and band selection for pixel classification using hyperspectral imagery data.
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.