A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept

In classifiers combination, the diversity rate among classifier's outputs is one of the most important discussions. There are different methods for combining classifiers. AdaBoost is an incremental method for creating a classifiers ensemble in which every AdaBoost algorithm has a local centrali...

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Bibliographic Details
Main Authors: Golestani, Abbas, Ahmadian, Kushan, Amiri, Ali, Jahed Motlagh, Mohammad Reza
Format: Conference Proceeding
Language:English
Subjects:
Boosting
Diversity methods
Diversity reception
Error correction
Information science
Testing
Training data
Online Access:Request full text
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Summary:In classifiers combination, the diversity rate among classifier's outputs is one of the most important discussions. There are different methods for combining classifiers. AdaBoost is an incremental method for creating a classifiers ensemble in which every AdaBoost algorithm has a local centrality. It means that classifiers are data biased and classify special data. In this paper we intend to find a new method for combining classifiers by using AdaBoost method and diversity concept. We have checked this method over different data sets and compared results of this method with others. These results indicate that we can develop other versions of this method for achieving a better performance.
DOI:10.1109/ICIS.2007.37