|
Diversity versus Quality in Classification Ensembles based on Feature Selection |
Cunningham, Padraig; Carney, John G.
|
|
|
|
TCD-CS-2000-02 Feature subset-selection has emerged as a useful technique for creating
diversity in ensembles ? particularly in classification ensembles. In this paper
we argue that this diversity needs to be monitored in the creation of the
ensemble. We propose an entropy measure of the outputs of the ensemble
members as a useful measure of the ensemble diversity. Further, we show that
using the associated conditional entropy as a loss function (error measure)
works well and the entropy in the ensemble predicts well the reduction in error
due to the ensemble. These measures are evaluated on a medical prediction
problem and are shown to predict the performance of the ensemble well. We
also show that the entropy measure of diversity has the added advantage that it
seems to model the change in diversity with the size of the ensemble.
|
|
Keyword(s):
|
Computer Science |
Publication Date:
|
2000 |
|
Type:
|
Report |
|
Peer-Reviewed:
|
Unknown |
|
Language(s):
|
English |
|
Institution:
|
Trinity College Dublin |
|
Citation(s):
|
Cunningham, Padraig; Carney, John G. 'Diversity versus Quality in Classification Ensembles based on Feature Selection'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2000-02, 2000, pp9 |
|
Publisher(s):
|
Trinity College Dublin, Department of Computer Science |
|
File Format(s):
|
application/pdf |
|
First Indexed:
2014-05-13 05:27:49 Last Updated:
2015-04-10 05:14:04 |
