This paper proposes a fuzzy discriminant analysis to solve the two-group classification problem where the measured variables are linguistic in nature. Especially under imprecise framework, the linguistic variables capture more information although vagueness is inherent. In analogy to classical statistics, a fuzzy linear discriminant function is introduced here, which directly deals with continu...

Nowadays, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. But, CLV calculation encounters some difficulties which limit the usage of this technique. Nonetheless, companies looking for methods to know how to calculate their customers’ CLV. In this paper, fuzzy classification rules we...

In this paper, the concept of extended intersection and restricted union of intuitionistic fuzzy soft sets are introduced. Some operations on intuitionistic fuzzy soft sets are investigated, and we prove that De Morgan’s laws hold in intuitionistic fuzzy soft sets theory. Based on these properties, we discuss the algebraic structures of intuitionistic fuzzy soft sets, which is lattice structure...

In this paper, we use fuzzy rule-based classification systems for classify cells of the Eimeria of Domestic Fowl based on Morphological Data. Thirteen features were extracted of the images of the cells, these features are genetically processed for learning fuzzy rules and a method reward and punishment for tuning the weights of the fuzzy rules. The experimental results show that our classifier ...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

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 effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The conseq...

the major aim of processing satellite images is to prepare topical and effectivemaps. the selection of appropriate classification methods plays an important role. amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. in present study, tm images of 1987, and etm+ images of 2000 and 2006were analyzed using artificial fuzzy artmap neural netwo...

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracyinterpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning process and a rule selection process can be combined to obtain a set of solutions with different trade-offs between the accuracy and the compactness of models. Nevertheless, an initial model needs to be defined, i...

In this work we use the Choquet integral as an aggregation function and we apply it in the fuzzy reasoning method of fuzzy rule-based classification systems. We study the behaviour of several fuzzy measures and we propose a genetic learning method of an appropriate fuzzy measure to model the interaction among the set of rules of each class. In the experimental study we show that the new proposa...