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This paper proposes the use of fuzzy if-then rules for Keystroke identification. The proposed methodology modifies Ishibuchi's genetic fuzzy classifier to handle high dimensional problems such as keystroke identification. High dimensional property of a problem increases the number of rules with low fitness. For decreasing them, rule initialization and coding are modified. Furthermore a new heuristic...
A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
The performances of conventional crisp and fuzzy K-nearest neighbor (K-NN) algorithms trained using finite samples tends to be poor . With ldquoholesrdquo in the training data, it is unlikely that the decision area formed can actually represent the underlying data distribution. There is a need to capture more useful information from the limited training samples, therefore we propose a new fuzzy rule-based...
A kernel fuzzy classifier with KFCMC and GA is proposed in this paper. For such classifier, firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. Then in the feature space, training samples are divided into some clusters by proposed KFCMC algorithm. For each created cluster, a fuzzy rule is defined. Some parameters of fuzzy classifier...
This paper presents a hybrid TSK fuzzy rule-based classifier. Fuzzy c-means clustering and genetic algorithm and are used to optimize the number of rules and antecedent parameters. By using the relationship between a SVM and a TSK FLS, an efficient method for learning the consequent parts of the TSK fuzzy system is introduced. The resulting hybrid fuzzy classifier has a compact rule base and good...
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