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
Processing and classification of protein mass spectra
TL;DR: This study focuses on the data-analytical phase, which takes as input mass spectra of biological specimens and discovers patterns of peak masses and intensities that discriminate between different pathological states.
Journal ArticleDOI
Feature selection for interpatient supervised heart beat classification
TL;DR: Feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models and indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.
Journal ArticleDOI
Integrated analysis of established and novel microbial and chemical methods for microbial source tracking.
Anicet R. Blanch,Lluís A. Belanche-Muñoz,Xavier Bonjoch,James Ebdon,Christophe Gantzer,Francisco Lucena,Jakob Ottoson,Christos Kourtis,Aina Iversen,Inger Kühn,Laura Moce,Maite Muniesa,J. Schwartzbrod,Sylvain Skraber,Georgios T. Papageorgiou,Huw Taylor,J.L. Wallis,Joan Jofre +17 more
TL;DR: Several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatics coliphage and the ratio
Journal ArticleDOI
Predicting academic success in higher education: literature review and best practices
Eyman Alyahyan,Dilek Düştegör +1 more
TL;DR: This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success, and will provide to educators an easier access to datamining techniques, enabling all the potential of their application to the field of education.
Proceedings ArticleDOI
New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques
TL;DR: A data-driven approach to the modeling of turbulent and transitional flows is proposed, to infer the functional form of deficiencies in known closure models by applying inverse problems to computational and experimental data, use machine learning to reconstruct the improved functional forms, and to inject the improvedfunctional forms in simulations to obtain more accurate predictions.
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.
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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.
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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.