Feature Selection for Automatic Classification of Non-Gaussian Data

@article{Foroutan1987FeatureSF,
  title={Feature Selection for Automatic Classification of Non-Gaussian Data},
  author={Iman Foroutan and Jack Sklansky},
  journal={IEEE Transactions on Systems, Man, and Cybernetics},
  year={1987},
  volume={17},
  pages={187-198}
}
A computer-based technique for automatic selection of features for the classification of non-Gaussian data is presented. The selection technique exploits interactive cluster finding and a modified branch and bound optimization of piecewise linear classifiers. The technique first finds an efficient set of pairs of oppositely classified clusters to represent the data. Then a zero-one implicit enumeration implements a branch and bound search for a good subset of features. A test of the feature… 
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96 Citations
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96 Citations

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Computational discovery for nonlinear classifiers
  • J. Sklansky
  • Computer Science
    [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics
  • 1992
TLDR
The author describes an approach to computer-aided discovery of nonlinear classifiers and cluster finders for complex distributions of multidimensional data by combining algebraic transformations, hill climbing, and genetic search.
Comparison of algorithms that select features for pattern classifiers
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
TLDR
The main aim of this paper is to present ways to improve RBFS algorithm which is a feature selection algorithm, and propose a new algorithm called ARS algorithm, which is to decrease the number of the decision-relative reducts for a decision table.
Optimum feature selection for decision functions
TLDR
An optimum feature selection method which is applicable to arbitrary (nonlinear) decision functions is presented and numerical examples of feature selection for a linear and a quadratic decision function are presented.
Designing Relevant Features for Continuous Data Sets Using ICA
TLDR
A novel approach to reducing dimensionality of the feature space by employing independent component analysis (ICA) is introduced, which efficiently builds a reduced set of features without loss in accuracy and also has a fast incremental version.
Pattern classification in dynamic environments: tagged feature-class representation and the classifiers
  • Qiuming Zhu
  • Computer Science
    IEEE Trans. Syst. Man Cybern.
  • 1989
The author discusses: a tagged feature and class representation of the pattern recognition problem in a dynamic environment; univariate cooperative classifiers that are based on statistical feature…
A Framework for Categorize Feature Selection Algorithms for Classification and Clustering
TLDR
A categorizing framework consisting of the procedures of finding selected subsets, including Search-based procedures and non-search based, evaluation criteria and data mining tasks will be completed and developed.
Genetic Algorithms for Classification and Feature Extraction
TLDR
Two GA-based approaches are developed, both utilizing a feedback linkage between feature evaluation and classification, and combine a GA with two different approaches: the KNearest-Neighbor decision rule and a production decision rule.
FEATURE SELECTION FOR CLASSIFICATION BY USING A GA-BASED NEURAL NETWORK APPROACH
TLDR
The genetic algorithm optimizes a feature vector by removing both irrelevant and redundant features and finds optimal ones and the results suggest that GA based neural classifiers are robust and effective in finding optimal subsets of features from large data sets.
Selection of the optimal prototype subset for 1-NN classification
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