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Open AccessJournal ArticleDOI

Wrappers for feature subset selection

Ron Kohavi, +1 more
- 01 Dec 1997 - 
- Vol. 97, Iss: 1, pp 273-324
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.
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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.

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Citations
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Journal ArticleDOI

Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products

TL;DR: RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets, and it is shown how selection probabilities and features co-occurrence can be used to highlight the most relevant features for discrimination.
Journal ArticleDOI

Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework

TL;DR: This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework that incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets.
Journal ArticleDOI

Filter versus wrapper gene selection approaches in DNA microarray domains

TL;DR: The application of a gene selection process is proposed, which also enables the biology researcher to focus on promising gene candidates that actively contribute to classification in these large scale microarrays, by an extensive comparison with more popular filter techniques.
Journal ArticleDOI

Data analysis for electronic nose systems

TL;DR: This review covers aspects of analysis from data normalisation methods to pattern recognition and classification techniques, and focuses on the use of artificial intelligence techniques such as neural networks and fuzzy logic for classification and genetic algorithms for feature (sensor) selection.
Proceedings ArticleDOI

ACCessory: password inference using accelerometers on smartphones

TL;DR: It is shown that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median) and unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes.
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.
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

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

J. R. Quinlan
- 25 Mar 1986 - 
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.