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Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base, and second, a weight is assigned...
Evolutionary multiobjective optimization (EMO) algorithms have often been used to search for a number of non-dominated fuzzy rule-based classifiers with respect to their accuracy and complexity. It is, however, pointed out in some studies that the entire accuracy-complexity tradeoff surface is not always found by well-known and frequently-used EMO algorithms such as NSGA-II. Especially it is very...
Data mining is a very active and rapidly growing research area in the field of computer science. Its goal is to obtain useful knowledge for users from a database. Association rule mining from a database is one of the most well-known data mining techniques. In general, a large number of if-then rules are extracted by specifying minimum support and confidence levels. They are, however, too complicated...
Genetic fuzzy rule selection has been successfully used to design accurate and interpretable fuzzy classifiers from numerical data. In our former study, we proposed its parallel distributed implementation which can drastically decrease the computational time by dividing both a population and a training data set into sub-groups. In this paper, we examine the effect of data reduction on the generalization...
This paper introduces fuzzy support vector machines (FSVMs) for Japanese dependency analysis. Japanese dependency analysis based on support vector machines (SVMs) has been proposed and has achieved high accuracy. While regular SVMs try to find a decision hyperplane from two distinct classes of the input examples, FSVMs apply a fuzzy membership to each input example such that different examples can...
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...
We study the problem of detecting and profiling terrorists using a combination of an ensemble classifier, namely random forest and relational information. Given a database for a set of individuals characterized by both "local" attributes such as age and criminal background, and "relational" information such as communications among a subset of the individuals, with a subset of the...
This paper considers the automatic design of fuzzy-rule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this...
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