Showing papers on "Rough set published in 1990"

Rough fuzzy sets and fuzzy rough sets

Abstract: The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set, sometimes with a view to prove that one is more general, or, more useful than the other. In this paper we argue that both notions aim to different purposes. Seen this way, it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems. First, one may think of deriving the upper and lower approximations of a fuzzy set, when a reference scale is coarsened by means of an equivalence relation. We then come close to Caianiello's C-calculus. Shafer's concept of coarsened belief functions also belongs to the same line of thought. Another idea is to turn the equivalence relation into a fuzzy similarity relation, for the modeling of coarseness, as already proposed by Farinas del Cerro and Prade. Instead of using a similarity relation, we can start with fuzzy granules which make a fuzzy partition of the reference scale. The main contribut...

Sensitivity analysis of rough classification

Abstract: Rough classification of patients after highly selective vagotomy (HSV) for duodenal ulcer is analysed from the viewpoint of sensitivity of previously obtained results to minor changes in the norms of attributes. The norms translate exact values of pre-operating quantitative attributes into some qualitative terms, e.g. “low”, “medium” and “high”. An extensive computational experiment leads to the general conclusion that original norms following from medical experience were well defined, and that the results of analysis of the considered information system using rough sets theory are robust in the sense of low sensitivity to minor changes in the norms of attributes.

Rough sets: probabilistic versus deterministic approach

Abstract: W: B. Gains and J. Boose, editors, Machine Learning and Uncertain Reasoning 3, pages 227-242. Academic Press, New York, NY, 1990. see also: International Journal of Man Machine Studies 29 (1988) 81-85

Rough sets approach to the analysis of the structure-activity relationship of quaternary imidazolium compounds.

Abstract: The relationship between chemical structure and antimicrobial activity of 201 quaternary imidazolium compounds is analysed using the theory of rough sets. The compounds are described by 8 attributes concerning structure and are divided into 5 classes of activity. The description builds up on information system. Using the rough sets approach, first a minimum set of 4 attributes significant for a high quality of classification has been found. The analysis of distribution of values of significant attributes in the best and worst class led to the definition of typical representatives of best and worst compounds in terms of significant attributes. Finally, a decision algorithm has been driven from information system, showing important relations between structure and activity. It may be helpful in supporting decisions concerning the synthesis of new antimicrobial compounds.

Inductive lerning in civil engineering : rough stes approach

Abstract: The paper discusses several potential civil engineering applications of inductive systems based on the theory of rough sets. This theory is discussed briefly. An inductive system is proposed for applications in knowledge acquisition for expert systems, for problem-solving, shallow modeling, learning about different domains, and in learning expert systems

A comparison of four tests for attribute dependency in the LEM and LERS systems for learning from examples

Abstract: The paper discusses two programs for learning from examples, LEM (Learning from Examples Module) and LERS (Learning from Examples based on Rough Sets). A few versions of both programs are implemented in Franz Lisp and are running on VAX 11/780. Both programs' main task is to automate knowledge acquisition for expert systems. Hence, they produce rules in the minimal discriminant form. The main problem addressed in the paper is the selection of the best mechanism for determining coverings, the minimal sets of relevant attributes. Four different methods, based on indiscernibility relation, partition, characteristic set and lower boundary are compared. Both theoretical analysis and experimental results of multiple running of many sets of examples, with variable number of examples and with variable number of attributes are taken into account. As a result the partition method is determined to be the most efficient way to compute coverings.

Generalized Cardinal Numbers and Their Ordering

Abstract: In this paper a general theory of power for hardly characterizable objects as well as related generalized cardinal numbers are presented. The attention is focused on the questions of order. Łukasiewicz logic is used as a supporting logic. The theory refers both to fuzzy sets and twofold fuzzy sets, partial sets, rough sets, etc.

Rough sets and dependency analysis among attributes in computer implementations of expert's inference models

Abstract: It is proposed, in the case when no proper mathematical model is obtainable, to use human experts' inference models in computer control algorithms. The notion of inference model is introduced and it is demonstrated that the formal apparatus of rough sets theory can be used to identify, to analyse and to evaluate this model. A method of computer implementation of such inference models is presented. The method is based on the analysis of dependencies among decision, measurable and observable attributes. PROLOG is proposed as the language of the model implementation. Formal considerations, the proposed approach and notions introduced are illustrated with a real-life example. It concerns a computer implementation of inference model of a rotary clinker kiln stoker. The model was used to control the process and an analysis of control results is presented.