Incremental Learning of Ensemble Classifiers on ECG Data

Authors:
Jan Macek
Affiliations:
University College Dublin
Venue:
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Year:
2005

Citing 0
Cited 3

Learn++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes

IEEE Transactions on Neural Networks
Incremental learning of support vector machines by classifier combining

PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We develop novel methods of incremental learning based on the bagging and boosting approaches to ensemble learning. Our method combines perceptron decision trees obtained with a margin maximizing algorithm into an ensemble in an incremental way. We demonstrate practical functionality of our algorithm on the task of ECG records classification. Our results are promising since comparable or superior accuracy is achieved when compared with results obtained by other existing methods of classification of ECG records, namely with the C5.0 decision tree algorithm.