The training of neural classifiers with condensed datasets

Authors:
S. H. Choi;P. Rockett
Affiliations:
Dept. of Electron. & Electr. Eng., Univ. of Sheffield;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2002

Citing 0
Cited 11

Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer

Journal of the American Society for Information Science and Technology
Neighborhood Property--Based Pattern Selection for Support Vector Machines

Neural Computation
Rough Set Approach for Generation of Classification Rules of Breast Cancer Data

Informatica
A Margin Maximization Training Algorithm for BP Network

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Multitask Learning with Data Editing

IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Improving performance of neural classifiers via selective reduction of target levels

Neurocomputing
Response modeling with support vector machines

Expert Systems with Applications: An International Journal
Designing Model Based Classifiers by Emphasizing Soft Targets

Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Fast pattern selection for support vector classifiers

PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Improving learning by using artificial hints

Neurocomputing
Designing Model Based Classifiers by Emphasizing Soft Targets

Fundamenta Informaticae - Advances in Artificial Intelligence and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we apply a k-nearest-neighbor-based data condensing algorithm to the training set of multilayer perceptron neural networks. By removing the overlapping data and retaining only training exemplars adjacent to the decision boundary we are able to significantly speed the network training time while achieving an undegraded misclassification rate compared to a network trained on the unedited training set. We report results on a range of synthetic and real datasets that indicate that a training speed-up of an order of magnitude is typical