Output details
11 - Computer Science and Informatics
Bangor University
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Output 0 of 0 in the submission
Article title
A weighted voting framework for classifiers ensembles
Type
D - Journal article
Title of journal
Knowledge and Information Systems
Article number
-
Volume number
n/a
Issue number
n/a
First page of article
n/a
ISSN of journal
0219-3116
Year of publication
2013
URL
-
Number of additional authors
1
Additional information
<24>The importance of this work is in its theoretical nature. It is well known that the overwhelming amount of the classifier ensemble research is driven by empirical studies, while theory is rare and very welcomed. Here we propose a theoretical framework which joins together three of the most popular classifier ensemble combination methods (combiners): the majority vote, the weighted majority vote and the Naive Bayes. As a result of developing the framework, we discovered a new combiner (which we named "Recall") whose logical place is between the weighted majority and the Naive Bayes.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
1
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-