Are coalitions needed when classifiers make decisions?

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

Mixed-attitude three-way decision model for aerial targets: Threat assessment based on IF-VIKOR-GRA method

Assessing potential threats typically necessitates the use of a robust mathematical model, a comprehensive evaluation method and universal decision rules. A novel approach is utilized in this study to optimize existing threat assessment (TA) algorithms and three-way decision models (3WDMs) are leveraged that incorporate decision-theoretic rough sets (DTRSs) within dynamic intuitionistic fuzzy (IF) environments to create a mixed-attitude 3WDM based on the IF-VIKOR-GRA method in the context of aviation warfare. The primary objectives of this study include determining conditional probabilities for IF three-way decisions (3WDs) and establishing mixed-attitude decision thresholds. Both the target attribute and loss function are expressed in the form of intuitionistic fuzzy numbers (IFNs). To calculate these conditional probabilities, an IF technique is used to combine the multi-attribute decision-making (MADM) method VIKOR and the grey relational analysis (GRA) method, while also taking into account the risk-related preferences of decision-makers (DMs). Optimistic and pessimistic 3WDMs are developed from the perspectives of membership degree and non-membership degree, then subsequently integrated into the comprehensive mixed-attitude 3WDM. The feasibility and effectiveness of this methodology are demonstrated through a numerical example and by comparison to other existing approaches

Coalitions’ Weights in a Dispersed System with Pawlak Conflict Model

The article addresses the issues related to making decisions by an ensemble of classifiers. Classifiers are built based on local tables, the set of local tables is called a dispersed knowledge. The paper discusses a novel application of Pawlak analysis model to examine the relations between classifiers and to create coalitions of classifiers. Each coalition has access to some aggregated knowledge on the basis of which joint decisions are made. Various types of coalitions are formed—a strong coalitions consisting of a large number and significant classifiers, and a weak coalitions consisting of insignificant classifiers. The new contributions of the paper is a systematical investigation of the weights of coalitions that influence the final decision. Four different method of calculating the strength of the coalitions have been applied. Each of these methods consider another aspect of the structure of the coalitions. Generally, it has been experimentally confirmed that, for a method that correctly identifies the relations between base classifiers, the use of coalitions weights improves the quality of classification. More specifically, it has been statistically confirmed that the best results are generated by the weighting method that is based on the size of the coalitions and the method based on the unambiguous of the decisions