Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint Boosting Feature Selection for Robust Face Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Comparison of classification accuracy using Cohen's Weighted Kappa
Expert Systems with Applications: An International Journal
Feature Selection for Iris Recognition with AdaBoost
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
A review of feature selection techniques in bioinformatics
Bioinformatics
Expert Systems with Applications: An International Journal
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Feature Selection Based on AdaBoost in Video Surveillance System
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 04
Computer Methods and Programs in Biomedicine
A variant of Rotation Forest for constructing ensemble classifiers
Pattern Analysis & Applications
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Computer Methods and Programs in Biomedicine
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In biomedical studies, accuracy of classification algorithms used in disease diagnosis systems is certainly an important task and the accuracy of system is strictly related to extraction of discriminatory features from data. In this paper, we propose a new multi-class feature selection method based on Rotation Forest meta-learner algorithm. The feature selection performance of this newly proposed ensemble approach is tested on Erythemato-Squamous diseases dataset. The discrimination ability of selected features is evaluated by the use of several machine learning algorithms. In order to evaluate the performance of Rotation Forest Ensemble Feature Selection approach quantitatively, we also used various and widely utilized ensemble algorithms to compare effectiveness of resultant features. The new multi-class or ensemble feature selection algorithm exhibited promising results in eliminating redundant attributes. The Rotation Forest selection based features demonstrated accuracies between 98% and 99% in various classifiers and this is a quite high performance for Erythemato-Squamous Diseases diagnosis.