Classifier selection by clustering

Authors:
Hamid Parvin;Behrouz Minaei-Bidgoli;Hamideh Shahpar
Affiliations:
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Venue:
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Year:
2011

Citing 6
Cited 0

Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Random Forests

Machine Learning
Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms

IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Ensembles of Partitions via Data Resampling

ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an innovative combinational algorithm for improving the performance of classifier ensembles both in stabilities of their results and in their accuracies. The proposed method uses bagging and boosting as the generators of base classifiers. Base classifiers are kept fixed as decision trees during the creation of the ensemble. Then we partition the classifiers using a clustering algorithm. After that by selecting one classifier per each cluster, we produce the final ensemble. The weighted majority vote is taken as consensus function of the ensemble. We evaluate our framework on some real datasets of UCI repository and the results show effectiveness of the algorithm comparing with the original bagging and boosting algorithms.