C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Hybrid Search of Feature Subsets
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A General Framework of Feature Selection for Text Categorization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Bridging low-level features and high-level semantics via fMRI brain imaging for video classification
Proceedings of the international conference on Multimedia
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Feature selection (FS) is a major issue in developing efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of this paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of important issues with respect to the functionality of FSAs, such as: (a) their ability to identify relevant features, (b) the performance of the classification models developed on a reduced set of features, (c) the reduction in the number of features, and (d) the interactions between different FSAs with the techniques used to develop a classification model. The analysis considers a variety of FSAs and classification methods.