Feature subset selection supports the classification task by reducing the search space as well as by removing irrelevant and random features, which might compromise the resulting classification model. Decision trees perform an embedded feature selection as they select only the relevant features for the splitting of the datasets during the induction process. FUZZYDT is a fuzzy decision tree whic...

We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F-SVM approa...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

The increasing use of fuzzy classification methods to generalize environmental data has led to a persistent question of how to determine class membership values, as well as how to interpret these values once they have been determined. This paper integrates the above two problems as complementary aspects of the same data reduction process. Within this process, it is shown that a fuzzy classifica...

An approach based on multi-objective genetic algorithms is proposed to construct interpretable and precision fuzzy classification system from data. First, a multi-objective genetic algorithm is used to accomplish feature selection and dynamic grid partition with three objectives: maximization of precision, minimization of the number of features, and minimization of the number of fuzzy rules. Th...

In this paper, a method based on the particle swarm optimization (PSO) is proposed for pattern classification to select a fuzzy classification system with an appropriate number of fuzzy rules so that the number of incorrectly classified patterns is minimized. In the PSO-based method, each individual in the population is considered to automatically generate a fuzzy classification system for an M...

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...

Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Signif...

In this research, two techniques of pixel-based and object-based image analysis were investigated and compared for providing land use map in arid basin of Mokhtaran, Birjand. Using Landsat satellite imagery in 2015, the classification of land use was performed with three object-based algorithms of supervised fuzzy-maximum likelihood, maximum likelihood, and K-nearest neighbor. Nine combinations...

In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...