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
Design of multisensor fusion-based tool condition monitoring system in end milling
TL;DR: The experimental results show that the design of TCM based on the feature level fusion can significantly improve the accuracy of the tool condition classification.
Journal ArticleDOI
Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.
TL;DR: In this paper, the authors explored the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex.
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Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement
TL;DR: In this paper, a data-based approach for building a prediction model consisting of feature generation, feature selection and model identification and validation steps is proposed, where a multivariable linear regression models are used in predictions.
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Gene selection algorithms for microarray data based on least squares support vector machine.
TL;DR: The proposed gene selection approaches can provide gene subsets leading to more accurate classification results, while their computational complexity is comparable to the existing methods.
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Feature subset selection can improve software cost estimation accuracy
TL;DR: This study finds that COCOMO's estimates can be improved via WRAPPER- a feature subset selection method developed by the data mining community that significantly and dramatically improves COComO's predictive power.
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.
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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.
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.