Authors:
Mariele Lanes
1
;
Paula F. Schiavo
1
;
Sidnei F. Pereira Jr.
1
;
Eduardo N. Borges
1
and
Renata Galante
2
Affiliations:
1
Universidade Federal do Rio Grande, Brazil
;
2
Universidade Federal do Rio Grande do Sul, Brazil
Keyword(s):
Diversity, Stacking, Ensemble, Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Due to the growth of research in pattern recognition area, the limits of the techniques used for the classification task are increasingly tested. Thus, it is clear that specialized and properly configured classifiers are quite effective. However, it is not a trivial task to choose the most appropriate classifier for deal with a particular problem and set it up properly. In addition, there is no optimal algorithm to solve all prediction problems. Thus, in order to improve the result of the classification process, some techniques combine the knowledge acquired by individual learning algorithms aiming to discover new patterns not yet identified. Among these techniques, there is the stacking strategy. This strategy consists in the combination of outputs of base classifiers, induced by several learning algorithms using the same dataset, by means of another classifier called meta-classifier. This paper aims to verify the relation between the classifiers diversity and the quality of stackin
g. We have performed a lot of experiments which results show the impact of multiple diversity measures on the gain of stacking.
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