Abstract Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of labels. While theory, any is sufficient, practice, this has substantial impact on quality final prediction. Dynamic classifier denote idea that each instance to classify, which labels predicted dynamically chosen. The complexi...

The need to deduce interesting and valuable information from large, complex, information-rich data sets is common to many research fields. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category in a comprehensible way. Besides the classical approaches, many rule mining approaches use biologicallyinspired algorithms such as evolutionary algorithms and swarm int...

Classification is an important task in data mining and machine learning which has been studied extensively and has a wide range of applications. There are many classification problem occurs and need to be solved. There are different types of classification algorithms like tree-based, rule-based etc, are widely used. In this paper, a performance comparison of different rule-based classifiers acr...

The paper presents a new approach to the automatic data-based generation of fuzzy rules. This is based on a tree-oriented rule induction algorithm and rule pruning. The hypothesis generation applies a set of measures for evaluation of fuzzy rules with respect to approximation quality, importance, clearness etc. In order to improve exibility and interpretability linguistic hedges are used to cre...

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets ...

Fuzzy rule-based systems have shown a high capability of knowledge extraction and representation when modeling complex, nonlinear classification problems. However, they suffer from the so-called curse of dimensionality when applied to high dimensional datasets, which consist of a large number of variables and/or examples. Multiclassification systems have shown to be a good approach to deal with...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...