A taxonomy for texture description and identification
A taxonomy for texture description and identification
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
On the Nonlinearity of Pattern Classifiers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Look-ahead based fuzzy decision tree induction
IEEE Transactions on Fuzzy Systems
Configurable Hybrid Architectures for Character Recognition Applications
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Cluster-based pattern discrimination: A novel technique for feature selection
Pattern Recognition Letters
Localized feature selection for clustering
Pattern Recognition Letters
Issues in fast 3D reconstruction from video sequences
MIV'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Multimedia, Internet & Video Technologies - Volume 7
Fast 3D reconstruction and recognition
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Nucleus-level clustering for word-independent syllable stress classification
Speech Communication
Subspace clustering of images using ant colony optimisation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Issues in fast 3D reconstruction from video sequences
MACMESE'07 Proceedings of the 9th WSEAS international conference on Mathematical and computational methods in science and engineering
Information Sciences: an International Journal
Analysis and insights into the variable selection problem
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Two step ant colony system to solve the feature selection problem
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Feature selection by reordering
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
A study of applying dimensionality reduction to restrict the size of a hypothesis space
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Linear separability and classification complexity
Expert Systems with Applications: An International Journal
An accumulative points/votes based approach for feature selection
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Feature subset selection using separability index matrix
Information Sciences: an International Journal
Analysis of data complexity measures for classification
Expert Systems with Applications: An International Journal
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The performance of most practical classifiers improves when correlated or irrelevant features are removed. Machine based classification is thus often preceded by subset selection--a procedure which identifies relevant features of a high dimensional data set. At present, the most widely used subset selection technique is the so-called "wrapper" approach in which a search algorithm is used to identify candidate subsets and the actual classifier is used as a "black box" to evaluate the fitness of the subset. Fitness evaluation of the subset however requires cross-validation or other resampling based procedure for error estimation necessitating the construction of a large number of classifiers for each subset. This significant computational burden makes the wrapper approach impractical when a large number of features are present.In this paper, we present an approach to subset selection based on a novel definition of the classifiability of a given data. The classifiability measure we propose characterizes the relative ease with which some labeled data can be classified. We use this definition of classifiability to systematically add the feature which leads to the most increase in classifiability. The proposed approach does not require the construction of classifiers at each step and therefore does not suffer from as high a computational burden as a wrapper approach. Our results over several different data sets indicate that the results obtained are at least as good as that obtained with the wrapper approach.