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
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Journal ArticleDOI
Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.
References
More filters
Book
Knowledge Discovery in Databases
Gregory Piateski,William Frawley +1 more
TL;DR: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases, which spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.
Proceedings Article
The feature selection problem: traditional methods and a new algorithm
Kenji Kira,Larry A. Rendell +1 more
TL;DR: A new algorithm Rellef is introduced which selects relevant features using a statistical method and is accurate even if features interact, and is noise-tolerant, suggesting a practical approach to feature selection for real-world problems.
Book
Subset Selection in Regression
TL;DR: In this paper, Efroymson's algorithm was used to replace two variables at a time with all subsets using branch-and-bound techniques. But the results showed that one subset was better than another.