In this paper, the extended possibilistic fuzzification for classification is proposed. Similar approach with the use of fuzzy–rough fuzzification (Nowicki and Starczewski, 2017; Nowicki, 2019) allows to obtain one of three decisions, i.e. ”yes”, ”no”, and ”I do not know”, The last label occurs when input information is imprecise, incomplete or in general uncertain, and consequently, determining the unequivocal decision is impossible. We extend three-way decision (Hu et al., 2017; Liu et al., 2016; Sun et al., 2017; Yao, 2010; Yao, 2011) into four-way decision by extending possibilistic fuzzification to the three–dimensional possibility and necessity measures of fuzzy events.

In this paper, a three-dimensional version of fuzzy-rough fuzzification is examined for classification tasks. Similar approach based on interval fuzzy-rough fuzzification has been demonstrated to classify with three decision labels of confidence, one of which were uncertain. The method proposed here relies on the use of fuzzification of inputs with a triangular membership function describing the nature of imprecision in data. As a result, we implement in fuzzy classifiers three dimensional membership functions using the calculus of general type-2 fuzzy sets. The approach is justified when more confidence labels are expected from the decision system, especially when the classifier is embedded in a recurrent hierarchical decision system working on easily available economic, extended, and advanced expensive real data.

In this paper we present a new method for content-based searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system learning we applied differential evolution. This last step is very important, and significantly improves the results. The presented algorithm can be easily modified, by changing its components (feature extractor or clustering algorithm).

In this paper a novel approach to automatic medical signal diagnosis is proposed. The authors propose a solution based on the application of Computational Intelligence (CI) to assist classification procedure based on Neural Network (NN) attempt. Experiments have been performed with the proposed CI solution applied on retrieved digital signals of various heart actions to help in missing or incomplete data gathering. Then this knowledge was applied as a training set in proposed NN classifier used to recognize potential dangers and therefore work as Decision Support System (DSS) designed for medical purposes.

The main problem addressed in this paper is to handle adequately imprecision of input data by means of a combination of fuzzy methods with the rough set theory. We will make use of fuzzy rough sets derived as rough approximations of fuzzy antecedent sets by non-singleton fuzzy premise sets in a fuzzy classifier. Adaptation of the parameters of this system will be done by the standard genetic algorithm.

The architecture of neuro-fuzzy systems with fuzzy rough sets originally has been developed to process with imprecise data. In this paper, the adaptation of those systems to the missing features case is presented. However, the main considerations concern with methods of learning which could be applied to such systems for approximation tasks. Various methods for determining values of system parameters have been considered, in particular the gradient learning method. The effectiveness of proposed methods has been confirmed by many simulation experiments, which results have been supplied to this paper.

Most methods constituting the soft computing concept can not handle data with missing or unknown feature values. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. In the paper we incorporate rough set theory to neuro-fuzzy system of very specific type. This results in learning systems which can work when the set number of available feature values is changing. To achieve better accuracy learning systems can be combined into larger ensembles. In the paper the AdaBoost metalearning is used to create an ensemble of learning systems. The rough-neuro-fuzzy systems use knowledge comprised in the form of fuzzy rules to perform classification. Simulations on a well-known benchmark give legitimacy to use the method in real world applications.

This paper presents a new approach to fuzzy classification in the case of missing data. The rough fuzzy sets are incorporated into Mamdani-type neuro-fuzzy structures, and the rough neuro-fuzzy classifier is derived. Theorems that allow the determination of the structure of a rough neuro-fuzzy classifier are given. Several experiments illustrating the performance of the rough neuro-fuzzy classifier working in the case of missing features are described.

Most methods constituting the soft computing concept can not handle data with missing or unknown features. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. To achieve better accuracy learning systems can be combined into larger ensembles. In this paper we combine logical neuro-fuzzy systems into the AdaBoost ensemble and extract fuzzy rules from the ensemble. The rules are used in rough-neuro-fuzzy classifier which can operate on data with missing values. The rough systems perform very well on these rules which was illustrated on a well known benchmark. The features were being removed to check the performance on incomplete data sets.

This paper presents a new approach to fuzzy classification in the case of missing features. The rough set theory is incorporated into neuro-fuzzy structures and the rough-neurofuzzy classifier is derived. The architecture of the classifier is determined by the MICOG (modified indexed center of gravity) defuzzification method. The structure of the classifier is presented in a general form which includes both the Mamdani approach and the logical approach - based on the genuine fuzzy aplications. A theorem, which allows to determine the structures of a roughneuro-fuzzy classifiers based on the MICOG defuzzification, is given and proven. Specific rough-neuro-fuzzy structures based on the Larsen rule, the Reichenbach and the Kleene-Dienes implications are given in details. In the experiments it is shown that the classifier with the Dubois-Prade fuzzy implication is characterized by the best performance in the case of missing features

Classification accuracy is nearly always improved after combining many systems. One of the most popular methods of multiple classification is boosting. In the paper we develop a method for merging fuzzy rule bases of neuro-fuzzy systems constituting an ensemble trained by the boosting algorithm.

This paper presents a new approach to fuzzy classification in case of missing information about object features. The rough set theory is incorporated into neuro-fuzzy structures and the rough-neuro-fuzzy classifier is derived. The architecture of the classifier is determined by the MICOG (modified indexed center of gravity) defuzzification method. Some illustrative examples are given.

In this paper, we present a neuro-fuzzy structure of the hierarchical prioritized structure (HPS) proposed by Yager. The HPS allows for easy hierarchization of a fuzzy rule-base. Our neuro-fuzzy system can be learned by the backpropagation algorithm and is relatively computationally efficient.