Fuzzy measures in determining seed points in clustering
Pattern Recognition Letters
The mean value of a fuzzy number
Fuzzy Sets and Systems - Fuzzy Numbers
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Ranking and defuzzification methods based on area compensation
Fuzzy Sets and Systems
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection toolbox software package
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Feature selection with neural networks
Pattern Recognition Letters
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature ranking and best feature subset using mutual information
Neural Computing and Applications
Selecting salient features for classification based on neural network committees
Pattern Recognition Letters
Neural Computing and Applications
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Multiple feature sets based categorization of laryngeal images
Computer Methods and Programs in Biomedicine
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
Towards fuzzy differential calculus part 3: Differentiation
Fuzzy Sets and Systems
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Integrated feature architecture selection
IEEE Transactions on Neural Networks
Neural-network feature selector
IEEE Transactions on Neural Networks
Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders
Artificial Intelligence in Medicine
Mining data with random forests: A survey and results of new tests
Pattern Recognition
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This paper is concerned with a feature selection technique capable of generating an efficient feature set in a few selection steps. The feature saliency measure proposed is based on two factors, namely, the fuzzy derivative of the predictor output with respect to the feature and the similarity between the feature being considered and the feature set. The use of the fuzzy derivative enables modelling the vagueness that occurs in estimating the predictor output sensitivity. The feature similarity measure employed allows avoiding utilization of very redundant features. The experimental investigations performed on five real world problems have shown the effectiveness of the feature selection technique proposed. The technique developed removed a large number of features from the original data sets without reducing the classification accuracy of a classifier. In contrast, the accuracy of the classifiers utilizing the reduced feature sets was higher than those exploiting all the original features.