This paper introduces a novel framework for conflict analysis based on rough set theory, extending Pawlak’s classical model. We introduce the concept of bi-coalitions, defined as groups of agents that fully agree on a subset of issues. Unlike traditional alliance relations, bi-coalitions are constructed without reliance on numerical thresholds, enabling a crisp and interpretable representation of consensus. The paper proposes an algorithm for identifying bi-coalitions using an indiscernibility matrix. To quantify coalition coherence, we introduce two strength measures with optional weighting of issues to reflect domain-specific relevance. Furthermore, we develop a negotiation algorithm guiding the system toward consensus or stable partitions. The proposed model is empirically validated on two real-world conflict scenarios: the 2023 parliamentary elections in Poland and the Middle East geopolitical situation. These case studies demonstrate the model’s ability to uncover interpretable coalition structures and support dynamic consensus-building.

The paper explores the use of image preprocessing techniques to improve the classification of the number of lights turned on in the game Phasmophobia based on screenshots. A novel, publicly available dataset was created for this purpose, consisting of images captured under varied in-game conditions. The study examines the effects of color schemes, image sizes, normalization methods, and batch sizes on classification performance. Multiple preprocessing combinations were evaluated using a multi-layer perceptron model. Results indicate that the choice of color schema, normalization technique, and image resolution significantly influences classification accuracy. In particular, combinations such as subtractive color model used in printing, color separation (CMYK) with standard normalization at 64×64 resolution yielded the highest performance. Lower resolutions yield better results on average, suggesting that higher detail does not necessarily improve classification. Among normalization techniques, robust scaling and z-score normalization achieve the highest average accuracy, while transformations like discrete cosine transform and fast fourier transform perform significantly worse. These findings, based on averaged results over hundreds of configurations, highlight the impact of preprocessing choices on model performance.

Accurately predicting the outcomes of football matches presents a complex challenge due to the dynamic and multifactorial nature of the sport. This study investigates the effectiveness of ensemble learning models with embedded feature selection – Random Forest, XGBoost, and LightGBM – compared to a traditional Support Vector Machine classifier combined with explicit feature selection techniques, including LASSO, PCA, RFE, and ANOVA. Using a constructed dataset based on pre-match statistics from the English Premier League, we evaluate each model’s performance in predicting home versus away wins. Results from experiments reveal that ensemble methods offer slightly superior performance. However, SVM models with feature engineering approaches such as LASSO and ANOVA also perform competitively. These findings suggest that both embedded and explicit feature selection strategies can be effective, and the choice of model may depend on practical considerations such as interpretability and computational cost.

This paper addresses the problem of predicting irradiation and, consequently, predicting solar panel production in Poland. The work uses deep learning for this purpose. It is shown that the quality of prediction is highly dependent on the network structure used in a given geographical location. The paper proposes a fast algorithm for finding the optimal network structure depending on geographical location. It has been experimentally shown that the proposed approach determines network structures close to the optimal ones that are determined by using full search. For this purpose, real data for selected seven cities in Poland are used. These cities are very diverse in terms of geographical location and territory characteristics.

The paper delves into the challenge of classification using dispersed data gathered from independent sources. The examined approach involves local models as ensembles of random trees constructed based on local data and randomly selected attributes. In the proposed model, a conflict analysis is used to identify the coalitions of local models. Finally, four different strategies for generating final decisions are explored: utilizing coalitions with and without weights and allowing one or two of the strongest coalitions to make decisions. The paper demonstrates that, regardless of the chosen method for making the final decision, the proposed model with conflict analysis and coalitions obtained better results, particularly in terms of the F1 measure and accuracy, compared to approaches from the literature, such as random forest or a single tree generated based on each local table separately with majority voting.

In the paper, a classification model for dispersed data is proposed. By dispersed data we mean a set of local tables that are collected independently by different units. This model uses conflict analysis, which considers the similarity of the values of conditional attributes occurring in decision classes of local tables. Tables having compatible values within classes are arranged into coalitions. The paper proposes the use of a parameter that steers the intensity of conflict between tables that are in allied relation. It was experimentally confirmed using 25 dispersed data that the proposed approach gives better results than the baseline approach in which coalitions are not used. The statistical significance of the differences was also proven. In addition, an advanced analysis of the impact of the parameter’s value steering the allied relations on the form of coalitions and the quality of classification was carried out.

The paper deals with dispersed data stored in independent local decision tables. We assume that there are the same conditional attributes in all tables. The paper proposes a new approach to generate global reducts based on data stored in all dispersed tables. The federated learning approach is used to ensure data protection and privacy. Based on the global reducts, decision rules are generated for each local table. Finally, the global classifier is composed of the set of all decision rules. In the paper, the proposed approach is compared with the baseline approach, in which the local reducts and local rules are generated for each individual table separately. The final decision is made using majority voting of the local models. It was shown that the proposed approach using federated learning provides better classification quality than the baseline approach.

In decision tree building, the choice of the splitting criteria highly affects the quality of model that is developed. In this paper, decision tree models are developed on dispersed data using entropy measure and twoing criterion as the splitting criteria. Dispersed data in this sense has multiple independent local tables on which decision tree models are built. Prediction vectors are generated based on the local models and a final prediction is made from aggregation using majority voting. In effort to improve model quality ensemble method technique (bagging) is applied to build multiple models for each local table. The main purpose of this paper is to make a comparative study on the classification quality of decision tree models built on dispersed data using entropy and twoing splitting measure. The main observation is that when knowledge is highly dispersed in a lot of local tables, using twoing criterion in building decision tree models is better than using entropy measure.

In this article, a new classification method using neural networks for dispersed data from independent sources - local decision tables - is proposed. The presented method uses a modified k-nearest neighbors algorithm and a radial basis function neural network. Prediction vectors are generated by the modified k-nearest neighbors algorithm for all local decision tables, which are then passed to the neural network for a final decision. Comparative analysis of the error level and structural complexity were carried on the proposed method and a classification method which uses a modified k-nearest neighbors algorithm and a multi-layer perceptron. Results obtained shows that the proposed method generates unambiguous decisions and achieves lower error with less structural complexity as compared to the classification method which uses a modified k-nearest neighbors algorithm and the multi-layer perceptron.

In this paper a new method of fusion predictions obtained based on dispersed data is proposed. In the method a power index is used. This approach allows to calculate the real power of prediction vectors generated based on local data with using the k-nearest neighbors classifier. The use of two power indices: Shapley-Shubik and Banzhaf-Coleman power index is analyzed. The influence of k-parameter value and the value of quota in simple game on the classification accuracy is also studied. The obtained results are compared with the approach in which the power index was not used. It was found that the proposed method of using the power index improves the classification accuracy. Moreover, both analyzed power indices generate comparable results.

This article discusses issues related to decision making based on applying decision trees and bagging methods on dispersed knowledge. In dispersed knowledge, local decision tables possess data independently in fragments. In this study, sub-tables are further generated with bagging method for each local table, based on which the decision trees are built. These decision trees classify the test object, and a probability vector is defined over the decision classes for each local table. For each vector, decision classes with the maximum value of the coordinates are selected and final joint decisions for all local tables are made by majority voting. Quality of decision making has been observed to increase when bagging method as an ensemble method is combined with decision trees on independent dispersed data. An important criterion in building a decision tree is to know when to stop growing the tree (stop splitting). That is, at what minimum number of objects on a working node do we stop building the tree to ensure the best decision results. The contribution of the paper is to observe the influence a stop criterion (expressed in the number of objects in the node) for decision trees used in conjunction with bagging method on independent data sources. It can be concluded that in dispersed data set, the stop split criteria does not influence the classification quality much. The statistical significance of the difference in the mean classification error values was confirmed only for a very high stop criterion (0.1× number of objects in training set) and for a very low stop criterion (equal to two). There is no significant statistical difference in the classification quality obtained for the stop criterion values: 4, 6, 8 and 10. An interesting remark is that for some dispersed data sets, in the case of smaller number of local tables and larger number of bootstrap samples, better quality of classification is obtained for a small number of objects in the stop criterion (mostly for two objects). Only, at a significant increase in the minimum number of objects at which growth of trees is stopped is quality of classification affected. However, the gain in reducing the complexity for trees that we get when using the larger values of stop criterion is significant.

Cooperation and coalitions’ formation are usually the preferred behavior when conflict situation occurs in real life. The question arises: is this approach should also be used when an ensemble of classifiers makes decisions? In this paper different approaches to classification based on dispersed knowledge are analysed and compared. The first group of approaches does not generate coalitions. Each local classifier generate a classification vector based on the local table, and then one of the most popular fusion methods is used (the sum method or the maximum method). In addition, the approach in which the final classification is made by the strongest classifier is analysed. The second group of approaches uses a coalitions creating method. The final classification is generated based on the coalitions’ predictions by using the two, mentioned above, fusion methods. In addition, the approach is analysed in which the final classification is made by the strongest coalition. For both groups of approaches, with and without coalitions, methods based on the maximum correlation and methods based on the covering rules are considered. The main conclusion that is made in this article is as follows. When classifiers generate fair and rational classification vectors, it is better to consider a coalition-based approach and the fusion method that collectively takes into account all vectors generated by classifiers.

In this paper, a method for attribute selection is used in a dispersed decision-making system with dynamically generated disjoint clusters. The system that is used has been proposed in the earlier studies of the author. The aim of the paper is to apply in this system, the method of attribute selection that is based on the rough set theory. Another objective is to compare the results obtained with and without the use of attribute selection method.

In the paper issues related to the use of dispersed knowledge in medicine are discussed.The main aim of the article is to investigate the efficiency of inference of seven selected fusion methods in a dispersed decision-making system.The dispersed system was proposed by the author in previous papers.The examined fusion methods -the maximum rule, the minimum rule, the median rule, the sum rule, the probabilistic product method, the method that is based on the theory of evidence and the method that is based on decision templates -are well known from the literature.In the paper two medical data sets from the UCI repository were used.Based on the obtained results it was concluded that for one data set the maximum rule generates the best results, and for other data set better methods are the sum rule and the median rule.

The article provides an overview of different approaches to the methods of conflict analysis that are inspired by the model of Zdzisław Pawlak.In the first part of the paper, Pawlak's original model is described.In the second part, the model proposed by Skowron and Deja is discussed.In the third part, the model proposed by the authors is presented.

Issues that are related to decision making that is based on dispersed knowledge are discussed in the paper. A dispersed decision-making system that was proposed in the earlier paper of the author is used in this paper. In the system the process of combining classifiers in coalitions is very importa nt and negotiation is applied in the clustering process. The main aim of the article is to compare the results obtained using five different methods of conflict analysis in the system. All of these methods are used when the individual classifiers generate probability vectors over decision classes. The most popular methods are considered - a sum rule, a product rule, a median rule, a maximum rule and a minimum rule. An additional aim is to compare the results obtained with using a dispersed decision-making system with the results obtained when the prediction results are aggregated directly using the conflict analysis methods. Tests, that were performed on data from the UCI repository are presented in the paper. The best methods in a particular situation are also indicated. It was found that some methods do not generate satisfactory results when there are dummy agents in a dispersed data set. That is, there are undecided agents who assign the same probability value to many different decision values. Another conclusion was that the use of a dispersed system improves the efficiency of inference.

The paper includes a discussion of issues related to the process of global decision-making on the basis of information stored in several local knowledge bases. The local knowledge bases contain information on the same subject, but are defined on diff

This paper discusses the issues related to the conflict analysis method and the rough set theory, process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The value of the rough set theory and conflict analysis applied in practical decision support systems with complex domain knowledge are expressed. The furthermore examples of decision support systems with complex domain knowledge are presented in this article. The paper proposes a new approach to the organizational structure of a multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of knowledge inconsistencies elimination will be implemented for created groups. Global decisions will be made using one of the methods for analysis of conflicts.

The paper presents the process of taking global decisions on the basis of the knowledge of local decision systems, in which sets of conditional attributes are different but not necessarily disjoint. We propose the organization of local decision systems into a multi-agent system with a hierarchical structure. The structure of multi-agent systems and the theoretical aspects of the organization of the system are presented. An editing and a condensing algorithm have been used in the process of global decision making. Also a density-based algorithm has been used in the process of taking global decisions to resolve conflicts. Furthermore, the paper presents the results of experiments conducted using some data sets from UCI repository.

The paper presents the process of taking global decisions on the basis of the knowledge of local decision systems involving the mutually complementary observations of objects which can be mutually contradictory. The authors suggest the organization of local decision systems into a multi-agent system with a hierarchical structure. The structure of multi-agent systems and the theoretical aspects of the organization of the system are presented. A density-based algorithm has been used in the process of taking global decisions. Furthermore the paper presents the results of experiments conducted using realistic data.