The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Feature selection with neural networks
Pattern Recognition Letters
Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Selecting salient features for classification based on neural network committees
Pattern Recognition Letters
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-based pattern discrimination: A novel technique for feature selection
Pattern Recognition Letters
Multiple feature sets based categorization of laryngeal images
Computer Methods and Programs in Biomedicine
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
Neural Computing and Applications
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
Optimal ensemble construction via meta-evolutionary ensembles
Expert Systems with Applications: An International Journal
Classifier combination based on confidence transformation
Pattern Recognition
Search strategies for ensemble feature selection in medical diagnostics
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Random forests based monitoring of human larynx using questionnaire data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper is concerned with a two stage procedure for designing a sequential SVM committee and selecting features for the committee from multiple feature sets. It is assumed that features of one type comprise one feature set. Selection of both features and hyper-parameters of SVM classifiers comprising the committee is integrated into one learning process based on genetic search. The designing process focuses on feature selection for pair-wise classification implemented by the SVM. In the first stage, a series of pair-wise SVM are designed starting from the original feature sets as well as from sets created by simple random selection from the original ones. Outputs of the SVM are then converted into probabilities and used as inputs to the second stage SVM. When testing the technique in a three-class classification problem of voice data, a statistically significant improvement in classification accuracy was obtained if compared to parallel committees. The number of feature types and features selected for the pair-wise classification are class specific.