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This chapter presents the application of Computational Intelligence (CI) paradigms for supporting decision making processes. First, the three main CI techniques, i.e., evolutionary computing, fuzzy computing, and neural computing, are introduced. Then, a review of recent applications of CI-based systems for decision making in various domains is presented. The contribution of each chapter included...
In this paper we discuss the ranking of alternatives represented by elements of Atanassov’s intuitionistic fuzzy sets, to be called A-IFSs, for short. That is, alternatives are elements of the universe of discourse with a degree of membership and a degree of non-membership assigned. First, we show disadvantages of some approaches known from the literature, including a straightforward method based...
Fuzzy systems have been successfully used in the area of controllers for a long time. The Mamdani method is one of the most popular inference systems for practical applications. The main problem of Mamdani-type inference system and other fuzzy logic based controllers is how to gain the fuzzy rules the inference system based on. Several approaches have been proposed for automatic rule base identification...
Data mining, also known as knowledge discovery in databases, is the process of extracting desirable knowledge or interesting patterns from existing databases. As a specific form of knowledge, association reflects semantics in terms of relationships among attributes in databases, and has been widely studied recently. This chapter focuses on dealing with uncertainty in discovering association rules...
We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We provide a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude...
The fundamental feature of human-friendly decision-making models (such as those encountered in complex medical problems, economical or political systems, technical diagnostic of physical systems, etc.) is predominantly concerned with interpretability of resulting constructs. Interpretability comes hand in hand with the granular nature of conceptual entities which are sought as the generic building...
Managerial decision-making is a complex procedure which combines information both in numerical as well as in linguistic form. In this paper, we present a general decision-making methodology which utilizes several intelligent soft-computing techniques, namely the theory of evaluative linguistic expressions, perception-based logical deduction and fuzzy transform. These techniques fulfil the above requirement...
We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the essence of data that may be relevant for a business activity. The use of linguistic summaries provides tools for the verbalization of data analysis (mining) results which, in addition to the more commonly used visualization e.g. via a GUI,...
Computation intelligence paradigms including artificial neural networks, fuzzy systems, evolutionary computing techniques, intelligent agents and so on provide a basis for human like reasoning in medical systems. Approximate reasoning is one of the most effective fuzzy systems. The compositional rule of inference founded on the logical law modus ponens is furnished with a true conclusion, provided...
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