Małgorzata Przybyła-Kasperek

Global decision-making system with dynamically generated clusters

Abstract This paper discusses the issues related to the process of global decision-making on the basis of knowledge which is stored in a dispersed form (several local knowledge bases or classifiers). In the paper a decision-making system is described. In this system, the classification process of the test object can be divided into several steps. In the first step, we investigate how particular classifiers classify a test object. We describe this using probability vectors over decision classes. We cluster classifiers with respect to similarities of the probability vectors. For every cluster, we find a kind of combined information. Finally, we classify the given test object by voting among clusters, using the combined information from each of clusters. The paper proposes a new approach to the organization of the structure of a decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the classifiers are combined into groups called clusters in a dynamic way. We seek to designate groups of classifiers that classify the test object in a similar manner. The groups of classifiers are not disjoint sets. We use overlapping clusters because this is a more suitable representation of classification compatibility. It is assumed that, if the classifier classifies the test object in an ambiguous way, it should belong to several clusters. Then, a process of the elimination of inconsistencies in the knowledge is implemented in the created groups. Global decisions are made by using one of the methods for the analysis of conflicts.

A dispersed decision-making system - The use of negotiations during the dynamic generation of a system's structure

Two-stage process of creating coalitions in a dispersed decision system was applied.Three types of relationships were used - friendship, conflict and neutrality.Initial clusters are formed in the first stage - groups of classifiers in friendship.In the second stage (a negotiation stage) neutral classifiers play a crucial role.New approach of generating clusters has been compared to approach proposed in 22. The issues that are related to the process of global decision-making on the basis of knowledge which is stored in a dispersed form (several local knowledge bases or classifiers) are discussed in this paper. In a decision-making system, which is described in the paper, the classification process of the test object starts with an investigation of how particular classifiers classify a test object. We describe the views of classifiers by using probability vectors over decision classes. In the system, the process of combining classifiers in coalitions is very important. Negotiation is used in the clustering process. We define three types of relations between classifiers: friendship, conflict and neutrality. The clustering process consists of two stages. In the first step, the initial groups are created. These groups contain classifiers that are in a friendship relation. In the second stage, classifiers which are in neutrality relation are attached to the existing groups. In this paper, a formal description of the clustering process is presented and mathematical properties of functions, which are used, are described. For every cluster, we find a kind of combined information. Finally, we classify the given test object by voting among clusters, using the combined information from each of the clusters.In the paper a new way of creating clusters (with a negotiation stage) is compared to the approach presented in the paper (Przybyla-Kasperek and Wakulicz-Deja, 2014) 23 (without negotiations). There are significant differences between the clusters that are generated using these two approaches, which are shown in the paper. In the new approach, the clusters are more complex and better reconstruct and illustrate the views of the classifiers on the classification.

Application of Reduction of the Set of Conditional Attributes in the Process of Global Decision-making

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 different sets of conditional attributes that are not necessarily disjoint. A decision-making system, which uses a number of knowledge bases, makes global decisions on the basis of a set of conditional attributes specified for all of the local knowledge bases used. The paper contains a description of a multi-agent decision-making system with a hierarchical structure. Additionally, it briefly overviews methods of inference that enable global decision-making in this system and that were proposed in our earlier works. The paper also describes the application of the conditional attributes reduction technique to local knowledge bases. Our main aim was to investigate the effect of attribute reduction on the efficiency of inference in such a system. For a measure of the efficiency of inference, we mean mainly an error rate of classification, for which a definition is given later in this paper. Therefore, our goal was to reduce the error rate of classification.

Application of the Method of Editing and Condensing in the Process of Global Decision-making

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.

Global decision-making in multi-agent decision-making system with dynamically generated disjoint clusters

HighlightsSystem, which operates on the basis of dispersed knowledge is considered.New approach to the organization of the systems structure was proposed.The local knowledge bases will be combined into groups (coalitions) in a dynamic way.The elimination inconsistencies in the knowledge will be implemented in the groups.Global decisions will be made by using one of the methods for analysis of conflicts. This paper discusses the issues related to the process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The approach considered in this paper is very general because we do not assume any additional conditions on the sets of objects or the sets of conditional attributes of local knowledge bases.The paper proposes a new approach to the organization of the structure of 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 the elimination inconsistencies in the knowledge will be implemented in the created groups. Global decisions will be made by using one of the methods for analysis of conflicts.The paper includes the definition of a multi-agent decision-making system with dynamically generated clusters and a description of a global decision-making process. In addition, the paper presents the results of experiments carried out on data from the UCI repository.

The strength of coalition in a dispersed decision support system with negotiations

Issues related to decision making based on dispersed knowledge are discussed in the paper. A system using a process of combining classifiers into coalitions is used. In this article clusters that are generated using an approach with a negotiation stage are used. Such clusters are more complex and are better able to reconstruct the views of the agents on the classifications. However, a significant improvement is not obtained when we use these clusters without an additional enhancement to the method of conflict analysis. In order to take full advantage of the clustering method, the size and structure of the clusters should be taken into account. Therefore, the main aim of this paper is to examine the impact of the method of conflict analysis and the methods that are used to determine the individual weights of the clusters on the effectiveness of the inference in a system that has a negotiation stage. Four new methods for determining the strength of a coalition are proposed and compared. The tests, which were performed on data from the University of California, Irvine Repository, are presented. The results that were obtained are much better than in the case in which the strength of the clusters was not calculated. The approach that consists in the computation of the individual weights from the judgments of each cluster allowed the size and structure of the clusters to be taken into account. This in turn allowed us to take full advantage of a clustering method with a negotiation stage.

Multi-Agent Decision Taking System

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.

Selected Methods of Combining Classifiers, when Predictions are Stored in Probability Vectors, in a Dispersed Decision-making System

Issues that are related to decision making that is based on dispersed knowledge are discussed in the paper. A system, that was proposed in the article [12], is used in this paper. In the system the process of combining classifiers in coalitions is very important 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 these methods are used if the individual classifiers generate probability vectors over decision classes. The most popular methods are considered: a sum, a product, a median, a maximum and a minimum rules. In the paper, tests, which were performed on data from the UCI repository, are presented. The best methods in a particular situation are indicated. It was found out 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.

Methods Based on Pawlak’s Model of Conflict Analysis - Medical Applications

In the study, issues related to the decision-making process using knowledge that is accumulated in several local knowledge bases are considered. In order to analyze conflicts and to create coalitions of base classifiers, three modifications of Pawlak’s model were applied. A system that uses these three modifications was then used for two dispersed sets of medical data. The main aim of this study was to compare the structure of the coalitions that were created. In the paper, the quality of the classification of the system using the proposed modifications was also compared.

Global Decisions Taking Process, Including the Stage of Negotiation, on the Basis of Dispersed Medical Data

The article discusses the issues related to the decision-making system using dispersed knowledge. In the proposed system, the classification process of the test object can be described as follows. In the first step, we investigate how particular classifiers classify a test object. We describe this using probability vectors over decision classes. We cluster classifiers with respect to similarities of the probability vectors. In the paper a new approach has been proposed in which the clustering process consists of two stages and three types of relations between classifiers: friendship, conflict and neutrality are defined. In the first step initial groups are created. Such a group contains classifiers that are in friendship relation. In the second stage, classifiers which are in neutrality relation are attached to the existing groups. In experiments the situation is considered in which medical data from one domain are collected in many medical centers. We want to use all of the collected data at the same time in order to make a global decisions.