Alicja Wakulicz-Deja

Diagnose progressive encephalopathy applying the rough set theory

This paper describes the application of rough sets theory the problem of the deciding on necessity of further tests and the final decisions about progressive encephalopathy in a child. The final decisions requires a series of invasive tests. Hence it is essential to carry out an appropriate preliminary classification. The process of the preliminary classification of patients with the use of the rough sets theory as to reduce unnecessary tests is the topic of this paper.

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.

Rough sets approach to medical diagnosis system

Pawlaks Rough Sets Theory is one of many mathematical approaches to handle imprecision and uncertainty. The main advantage of the theory over other techniques is that it does not need any preliminary or additional information about analyzed data. This feature of rough set theory favors its usage in decision systems where new relations among data must be uncovered. In this paper we use data from a medical data set containing information about heart diseases and applied drugs to construct a decision system, test its classification accuracy and propose a methodology to improve an accurateness and a testability of generated “if-then” decision rules.

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.

Learning Fuzzy Cognitive Maps from the Web for the Stock Market Decision Support System

In this paper we would like to propose a new hybrid scheme for learning approximate concepts and causal relations among them using information available from the Web. For this purpose we are applying fuzzy cognitive maps (FCMs) as a knowledge representation method and an analytical tool. Fuzzy cognitive maps are a decision-support tool, analytical technique, and a qualitative knowledge representation method with large potential for real world applications. FCMs are able to express the behavior of a system through the description of cause and effect relationships among concepts. FCMs can be represented as directed graphs consisting of concepts (nodes) and cause and effect relationships (branches) among them. The concepts represent states that are observable within the domain. The directions of branches indicate the causal dependency between source and target concepts. In spite of a quite simple construction and relatively easy interpretation, which can play a key role while constructing decision support systems, its expected that FCMs can express complex behaviors of dynamic systems. The basic formalism of FCMs is presented in section 2. Obviously, there are also drawbacks of FCMs, that have been mentioned, e.g., in [4]. Also, it can be mentioned that, among the many extensions to FCMs, there is still lack of common formalism, which causes some difficulties when comparing one with another.

Attribute selection and rule generation techniques for medical diagnosis systems

Success of many learning schemes is based on selection of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the process model can result in poor predictive accuracy and increased computation. This paper shows that the accuracy of classification can be improved by selecting subsets of strong attributes. Attribute selection is performed by using the Wrapper method with several classification learners. The processed data are classified by diverse learning schemes and generated “if-then” rules are supervised by domain experts.

Decision Units as a Tool for Rule Base Modeling and Verification

Expert systems are problem solvers for specialized domains of competence in which effective problem solving normally requires human expertise. The transition of expert systems technology from research laboratories to software development centers highlighted the fact that the quality assurance for expert system is a very important issue for most real-word problems. Although the basic verification concepts are shared by software engineering and knowledge engineering, verification methods of conventional software are not directly applicable to expert systems and the new, specific methods of verification are required. The main aim of this work is to present our own rule base verification method. In our opinion the decision units conception allows us to consider different verification and validation issues together. Thanks to properties of the decision units we can perform different verification and validation actions during knowledge base development and realization.