Using a Neural Network to Approximate an Ensemble of Classifiers

Authors:
X. Zeng;T. R. Martinez
Affiliations:
Computer Science Department, Brigham Young University, 3366 TMCB, Provo, UT 84602, USA;Computer Science Department, Brigham Young University, 3366 TMCB, Provo, UT 84602, USA. E-mail: martinez@cs.byu.edu
Venue:
Neural Processing Letters
Year:
2000

Citing 7
Cited 4

Bagging predictors

Machine Learning
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Knowledge Acquisition form Examples Vis Multiple Models

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pruning Adaptive Boosting

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An empirical evaluation of bagging and boosting

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Multistage Neural Network Ensembles

MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Model compression

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Agent-Based Approach to Distributed Ensemble Learning of Fuzzy ARTMAP Classifiers

KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Artificial neural network reduction through oracle learning

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several methods (e.g., Bagging, Boosting) of constructing and combining an ensemble of classifiers have recently been shown capable of improving accuracy of a class of commonly used classifiers (e.g., decision trees, neural networks). The accuracy gain achieved, however, is at the expense of a higher requirement for storage and computation. This storage and computation overhead can decrease the utility of these methods when applied to real-world situations. In this Letter, we propose a learning approach which allows a single neural network to approximate a given ensemble of classifiers. Experiments on a large number of real-world data sets show that this approach can substantially save storage and computation while still maintaining accuracy similar to that of the entire ensemble.