In this contribution, the model which is dedicated to reducing the number of false positive instances is proposed. This is a self-tuning model using aggregation functions and time-series data periods. As a case study, the proposed model is tested in the context of phishing link detection. In the proposed model, well-known aggregation functions are applied to combine the confidence values of multiple Classification models for email phishing. The division of the dataset into multiple segments and subsets facilitates the implementation of incremental learning strategies. This approach enables the iterative enhancement of model performance through the training of new data while leveraging previously acquired knowledge. In our research, two datasets are considered, namely the existing PhiUSIIL phishing URL dataset as well as the dataset provided by the FreshMail company are applied. The proposed algorithm achieves a small number of expected false positives. This reduces the costs associated with manual analysis of such cases by domain experts (in the case of incorrect prediction as phishing mail).

In this contribution, the binary ensemble classifier based on kNN in the case of microarrays is discussed. There are considered diverse values of hyperparameters, such as the number of neighbors, the distance metric discussed, and other related types of hyperparameters. Moreover, interval modelling is used to improve the performance of the Classification. The algorithm is based on the interval-valued aggregation. To obtain several intervals, we use column-wise partitioning. Several kNN classifiers with different numbers of neighbors are used to compute certainty coefficients on each partition. Based on these values, intervals are determined. The interval-valued aggregation is treated as a hyperparameter of the model and is applied to combine obtained intervals. The performance of the proposed classifier is compared to the well-known ensemble classifier, which is a Bagging classifier. The results of the statistical tests prove that the proposed ensemble model may be effectively applied to the high-dimensional datasets, especially microarrays.

In the paper, we consider a combination of classifiers for the microarray datasets - examples of high dimensional datasets. Aggregation functions, in this case, are used to combine the output values of the constituent classifiers. Some known families of aggregation functions with several examples are studied and compared concerning their usefulness in the given classification method. Based on the proposed ranking of aggregations, the top aggregation functions considering their properties were explored to find which predispose to yield better classification results.

In this contribution there are considered interval-valued methods of improving the quality of classification by the k- N N binary classifiers in the case of large number of attributes in data sets. One of the possible applications of the introduced method are the microarray data so the presented results may be applied in medical diagnosis support, for example, in identification of marker genes. The proposed algorithm involving reduction methods of time complexity and interval modeling of data using interval-valued aggregation functions has a significantly higher classification quality than the other considered algorithm with the aggregation method involving the numerical arithmetic mean. The former algorithm is based on the interval-valued aggregation of uncertainty intervals determined on the basis of certainty coefficients of individual k- N N classifiers while the latter is based on the aggregation of certainty coefficients of individual k- N N classifiers with the use of the numerical arithmetic mean. Moreover, the performance of the new algorithm based on interval-valued methods was compared with the known from the literature classifiers in cancer diagnosis based on gene expression microarrays. The obtained results prove that in practical applications the newly proposed algorithm can be successfully used.

In this contribution we analyze a real DNS traffic collected at the University of Rzeszów campus. All DNS queries and responses observed in the entire network were gathered. Data include traffic generated by students, scholars, and other staff members as well as servers, IoT and all other devices connected to network. Data was collected using the Tshark network protocol analyzer and stored in a ClickHouse columnar-oriented database dedicated for high volume data analyses. Fuzzy C-means clustering was applied to analyze DNS traffic and to distinguish between human- and machine generated traffic. Analysis was performed on a representative sample containing 3 516 094 records and 33 proposed features.

A new concept of equivalence between intervals and the induced indistinguishability between interval-valued (IV) fuzzy sets are proposed and considered. A new notion of the degree of IV equivalence is presented where partial or linear orders and the width of intervals are involved reflecting uncertainty. Furthermore, construction methods of the considered equivalences are provided and the relation between them and other notions of IV equivalences are studied. Finally, a methodology to apply the proposed equivalences in the field of image processing is shown, along with an illustrative example.

In this contribution methods for improving the quality of multi-class classification by the k nearest neighborhood classifiers in the case of large number of missing values in data sets are considered. Two versions of classifiers are compared. In the first case the aggregation of certainty coefficients of the individual classifiers with the use of the arithmetic mean is applied. In the second case interval modelling and interval-valued aggregation functions are involved. It is proved that the classifier which uses interval methods entails a much slower decrease in classification quality.

In this contribution the concept of a local contrast of a fuzzy relation with the use of a consensus measure is introduced. A construction method of such local contrast using aggregation functions and fuzzy implications is considered. Other construction methods using similarity measure are also pointed out. Several examples of local contrasts are provided. The usability of introduced local contrast measures is evaluated by applying them in image processing for salient region detection.

Fuzzy relations are compared by membership values and as a consequence new types of local properties of fuzzy relations are introduced. In the new properties of fuzzy relations an arbitrary binary relation is involved. Particularly, a binary aggregation function may be used to define these properties. Connections between the new local properties of fuzzy relations are described. Furthermore, preservation of these properties in aggregation process is considered. Finally, notes on applications of the presented local properties in the context of fuzzy multisets and decision making are provided.

In this paper, the issue of measuring the degree of inclusion and equivalence measure for Atanassov intuitionistic fuzzy setting is considered. We propose an application of the inclusion and equivalence measures created by using the partial or linear order on Atanassov intuitionistic fuzzy setting. Moreover, some properties of inclusion and equivalence measures and some correlation between them and aggregation operators are examined and their possible application in image processing is indicated.

In this paper the problem of connections between input fuzzy relations Ri, ..., Rnon a set X and the output fuzzy relation Rf= F (Ri, ..., Rn) on X is studied, where F is a function of the type F : [0,1]n→ [0,1] and RF is an aggregated fuzzy relation. Namely, fuzzy relation RF= F(R1, ..., Rn) is assumed to have a given property and the properties of fuzzy relations Ri, ..., Rnare examined. This approach to checking connections between input fuzzy relations and the output fuzzy relation is a new one. In the literature the problem of preservation by an aggregation function F diverse types of properties of fuzzy relations Ri, ..., Rnis examined. The properties, which are examined in this paper, depend on their notions on binary operations B : [0,1]2→ [0,1], i.e. they are generalized versions of known properties of fuzzy relations.

In this paper we study interval-valued fuzzy relations. We consider preference relations, i.e. a triplet consisting of strict preference, indifference and incomparability which are defined with the use of a fuzzy negation. We analyze the preservation of the fuzzy negation based reciprocity property of interval-valued fuzzy relations by aggregation functions and by some basic interval-valued fuzzy relations. We use diverse representa-tions of aggregation functions. We also consider the connection between N-reciprocal relations and transitivity properties. We provide a numerical example where the final alternative is chosen with the use of generalized voting method, where admissible linear orders for intervals are applied.

Based on the definition of admissible order for interval-valued Atanassov intuitionistic fuzzy sets, we study ordered weighted averaging operators in these sets, distinguishing between the weights associated with the membership and those associated with the nonmembership degree, which may differ from the latter. We also study Choquet integrals for aggregating information, which is represented using interval-valued Atanassov intuitionistic fuzzy sets. We conclude with two algorithms to choose the best alternative in a decision-making problem when we use this kind of sets to represent information.

In the paper properties of intuitionistic fuzzy relations are considered and preservation of some properties by operations, including lattice operations, composition and related operators are studied. Properties of intuitionistic fuzzy relations, namely reflexivity, irreflexivity, connectedness, symmetry, antisymmetry, perfect antisymmetry and transitivity are considered. Moreover, the authors study assumptions under which intuitionistic fuzzy preference relations and related operators fulfil these properties.