Computer
Floating search methods in feature selection
Pattern Recognition Letters
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Investigation of a Novel Self-configurable Multiple Classifier System for Character Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
On the selection of decision trees in random forests
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Pattern Recognition Letters
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Hi-index | 0.00 |
Classifier subset selection (CSS) from a large ensemble isan effective way to design multiple classifier systems(MCSs). Given a validation dataset and a selectioncriterion, the task of CSS is reduced to searching thespace of classifier subsets to find the optimal subset. Thisstudy investigates the search efficiency of geneticalgorithm (GA) and sequential search methods for CSS.In experiments of handwritten digit recognition, we selecta subset from 32 candidate classifiers with aim to achievehigh accuracy of combination. The results show that inrespect of optimality, no method wins others in all cases.All the methods are very fast except the generalized plus land take away r(GPTA) method.