Results 1 - 10 of 34,189

An introduction to variable and feature selection

by Isabelle Guyon - Journal of Machine Learning Research , 2003
"... Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. ..."
Abstract - Cited by 1352 (16 self) - Add to MetaCart
Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.

Toward optimal feature selection

by Daphne Koller, Mehran Sahami - In 13th International Conference on Machine Learning , 1995
"... In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it g ..."
Abstract - Cited by 480 (9 self) - Add to MetaCart
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature

Adaptive floating search methods in feature selection

by P. Somol , P. Pudil , J. Novovicova , P. Paclik - PATTERN RECOGNITION LETTERS , 1999
"... A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer to the opti ..."
Abstract - Cited by 548 (21 self) - Add to MetaCart
A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer

Feature selection: Evaluation, application, and small sample performance

by Anil Jain, Douglas Zongker - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1997
"... Abstract—A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature s ..."
Abstract - Cited by 474 (13 self) - Add to MetaCart
Abstract—A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature

A Comparative Study on Feature Selection in Text Categorization

by Yiming Yang, Jan O. Pedersen , 1997
"... This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI), ..."
Abstract - Cited by 1320 (15 self) - Add to MetaCart
This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI

Feature Selection for Classification

by M. Dash, H. Liu - Intelligent Data Analysis , 1997
"... Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a com ..."
Abstract - Cited by 299 (9 self) - Add to MetaCart
Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a

An extensive empirical study of feature selection metrics for text classification

by George Forman, Isabelle Guyon, André Elisseeff - J. of Machine Learning Research , 2003
"... Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison ..."
Abstract - Cited by 496 (15 self) - Add to MetaCart
Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison

Feature selection for SVMs

by J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik - Advances in Neural Information Processing Systems 13 , 2000
"... We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard ..."
Abstract - Cited by 282 (17 self) - Add to MetaCart
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard

Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy

by Hanchuan Peng, Fuhui Long, Chris Ding - IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2005
"... Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first der ..."
Abstract - Cited by 571 (8 self) - Add to MetaCart
Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first

A review of feature selection techniques in bioinformatics

by Yvan Saeys, Iñaki Inza, Pedro Larrañaga - BIOINFORMATICS , 2007
"... Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed t ..."
Abstract - Cited by 358 (10 self) - Add to MetaCart
Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed
Results 1 - 10 of 34,189