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Title:
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A NEW EFFICIENT FUZZY DIVERSITY MEASURE IN CLASSIFIER FUSION |
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Author(s):
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Abbas Golestani , Javad Azimi , Morteza Analoui , Mohamad Kangavari |
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ISBN:
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978-972-8924-30-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Diversity, classifier ensembles, Fuzzy, Diversity Measures. |
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Type:
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Reflection Paper |
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First Page:
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722 |
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Last Page:
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726 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The combination of classifiers is one of the important and efficient algorithms in pattern recognition. In classifiers combination, the diversity rate among classifier outputs is one of the most important discussions. There are different methods calculating the diversity among classifiers. In this paper, we intend to find a new method for calculating the diversity among classifiers by using the fuzzy logic. We have checked this method over the total of different classifiers set and compare them with different methods. We will observe that according to the powerful credit of fuzzy discussions, this method has more advantages than the previous methods. |
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