Bagging different instead of similar models for regression and classification problems

Authors:
Sotiris B. Kotsiantis;Dimitris N. Kanellopoulos
Affiliations:
Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, 26504, Rio, Patras, Greece.;Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, 26504, Rio, Patras, Greece
Venue:
International Journal of Computer Applications in Technology
Year:
2010

Citing 15
Cited 0

Bagging predictors

Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Efficient Construction of Regression Trees with Range and Region Splitting

Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach

Data Mining and Knowledge Discovery
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Boosting Methods for Regression

Machine Learning
Stochastic gradient boosting

Computational Statistics & Data Analysis - Nonlinear methods and data mining
An Evaluation of Grading Classifiers

IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Tree induction vs. logistic regression: a learning-curve analysis

The Journal of Machine Learning Research
Bagging Equalizes Influence

Machine Learning
Memory-based morphological analysis

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Issues in stacked generalization

Journal of Artificial Intelligence Research
Combinations of weak classifiers

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Even though many ensemble techniques have been proposed, there is as yet no clear picture of which method is best. In this study, we propose a technique that uses different subsets of the same training dataset with the concurrent usage of a voting (for classification problems) or averaging methodology (for regression problems) for combining different learners instead of similar learners. We performed a comparison of the proposed ensemble with other well known ensembles that use the same base learners and the proposed technique had better accuracy in most cases.