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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A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
TL;DR: A short introduction into machine learning is given, its potential for precision crop protection is analyzed and an overview of instructive examples from different fields of precision agriculture is provided.
Journal ArticleDOI
Facial Attractiveness: Beauty and the Machine
TL;DR: Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.
Journal ArticleDOI
Differentiating between good credits and bad credits using neuro-fuzzy systems
TL;DR: In this paper, the authors compared the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans.
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Relevance assessment of full-waveform lidar data for urban area classification
TL;DR: The results show that the echo amplitude as well as two features computed from the radiometric calculation of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper.
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A Review of Feature Selection and Its Methods
B. Venkatesh,J. Anuradha +1 more
TL;DR: This paper focuses on a survey of feature selection methods and can conclude that most of the FS methods use static data, while the existing DR algorithms do not address the issues with the dynamic 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.
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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.