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Diversity is a key characteristic of a classifier ensemble. A classifier ensemble must be composed of base classifiers with different performance in different areas of the problem space. Several works studied different diversity measures by performing extensive numerical experiments. However, up to our knowledge, no method has been proposed to visualize the diversity of a classifier ensemble. In this...
In a preceding contribution, we proposed a novel combination method by means of a fuzzy linguistic rule-based classification system. The fuzzy linguistic combination method was based on a genetic fuzzy system in order to learn its parameters from data. By doing so the resulting classifier ensemble was able to show a hierarchical structure and the operation of the latter component was transparent to...
Fuzzy set theory has been widely and successfully used as a mathematical tool to combine the outputs provided by the individual classifiers in a multiclassification system by means of a fuzzy aggregation operator. However, to the best of our knowledge, no fuzzy combination method has been proposed, which is composed of a fuzzy rule-based system. We think this can be a very promising research line...
Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would...
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