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
More filters
Journal ArticleDOI
Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives
TL;DR: Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.
Journal ArticleDOI
Evolving feature selection
Huan Liu,Edward R. Dougherty,Jennifer G. Dy,Kari Torkkola,Eugene Tuv,Hanchuan Peng,Chris Ding,Fuhui Long,Michael E. Berens,Lance Parsons,Zheng Zhao,Lei Yu,George Forman +12 more
TL;DR: This article considers feature-selection overfitting with small-sample classifier design; feature selection for unlabeled data; variable selection using ensemble methods; minimum redundancy-maximum relevance feature selection; and biological relevance infeature selection for microarray data.
Journal ArticleDOI
Omic and Electronic Health Record Big Data Analytics for Precision Medicine
TL;DR: This work provides two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR, to demonstrate how big data analytics enables precision medicine.
Journal ArticleDOI
Mapping megacity growth with multi-sensor data
TL;DR: This paper presents an approach to map urban growth from multi-sensoral data, exemplified for the Dhaka megacity region in Bangladesh between 1990 and 2006, which is globally applicable and can facilitate regional urban growth maps in arbitrary complex and dynamic environments.
Journal Article
Using Markov Blankets for Causal Structure Learning
TL;DR: After running a series of comparative experiments on five artificial networks, it is argued that Markov blanket algorithms such as TC/TCbw or Grow-Shrink scale better than the reference PC algorithm and provides higher structural accuracy.
References
More filters
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.