Recursive Partitioning Technique for Combining Multiple Classifiers

Authors:
Terry Windeatt
Affiliations:
Centre for Vision, Speech and Signal Proc., School of EE, IT & Maths, University of Surrey, Guildford, Surrey, UK GU2 5XH. E-mail: t.windeatt@ee.surrey.ac.uk
Venue:
Neural Processing Letters
Year:
2001

Citing 8
Cited 1

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
Spectral technique for hidden layer neural network training

Pattern Recognition Letters
Strategies for combining classifiers employing shared and distinct pattern representations

Pattern Recognition Letters - special issue on pattern recognition in practice V
Spectral Techniques in Digital Logic

Spectral Techniques in Digital Logic
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On sequential construction of binary neural networks

IEEE Transactions on Neural Networks

Spectral coefficients and classifier correlation

MCS'03 Proceedings of the 4th international conference on Multiple classifier systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Various methods of reducing correlation between classifiers in a multiple classifier framework have been attempted. Here we propose a recursive partitioning technique for analysing feature space of multiple classifier decisions. Spectral summation of individual pattern components in intermediate feature space enables each training pattern to be rated according to its contribution to separability, measured as k-monotonic constraints. A constructive algorithm sequentially extracts maximally separable subsets of patterns, from which is derived an inconsistently classified set (ICS). Leaving out random subsets of ICS patterns from individual (base) classifier training sets is shown to improve performance of the combined classifiers. For experiments reported here on artificial and real data, the constituent classifiers are identical single hidden layer MLPs with fixed parameters.