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Classifier design for a classification problem with M classes can be viewed as finding an optimal partition of its pattern space into M disjoint subspaces. However, this is not always a good strategy especially when training patterns from different classes are heavily overlapping in the pattern space. A simple but practically useful idea is the use of a reject option. In this case, the pattern space...
Fuzzy genetics-based machine learning (FGBML) has frequently been used for fuzzy classifier design. It is one of the promising evolutionary machine learning (EML) techniques from the viewpoint of data mining. This is because FGBML can generate accurate classifiers with linguistically interpretable fuzzy if-then rules. Of course, a classifier with tens of thousands of if-then rules is not linguistically...
A large number of non-dominated fuzzy rule-based classifiers are often obtained by applying a multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm to a pattern classification problem. The obtained set of non-dominated classifiers can be used to analyze their accuracy-interpretability tradeoff relation. One important issue, which has not been discussed in many studies on MoFGBML,...
A multi-objective evolutionary fuzzy rule selection process extracts a subset of fuzzy rules from an initial set, by applying a multi-objective evolutionary algorithm. Two approaches can be used to determine the number of terms (i.e. the granularity) associated with the linguistic variables that appear in the rules: a pre-established single granularity can be chosen, or a multiple granularities approach...
Tens of thousands of classifiers have been proposed so far. There is no best classifier among them for all the existing data sets. The performance of each classifier often depends on the data sets used for comparison. Even for a single classifier, suitable parameters of the classifier also depend on the data sets. That is, there is a possibility that a suited classifier and its parameter specification...
Explanation ability of a fuzzy rule-based classifier is its ability to explain why an input pattern is classified as a particular class in a convincing way. This ability is important especially when fuzzy rule-based classifiers are used as support systems for human users. This is because human users often want to know why the current input pattern is classified as a particular class. The explanation...
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