Showing papers on "Rough set published in 1988"

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

Knowledge Acquisition under Uncertainty- a Rough Set Approach

Abstract: The paper describes knowledge acquisition under uncertainty using rough set theory, a concept introduced by Z. Pawlak in 1981. A collection of rules is acquired, on the basis of information stored in a data base-like system, called an information system. Uncertainty implies inconsistencies, which are taken into account, so that the produced rules are categorized into certain and possible with the help of rough set theory. The approach presented belongs to the class of methods of learning from examples. The taxonomy of all possible expert classifications, based on rough set theory, is also established. It is shown that some classifications are theoretically (and, therefore, in practice) forbidden.

Rough sets and information retrieval

Abstract: The theory of rough sets was introduced [PAWLAK82]. It allows us to classify objects into sets of equivalent members based on their attributes. We may then examine any combination of the same objects (or even their attributes) using the resultant classification. The theory has direct applications in the design and evaluation of classification schemes and the selection of discriminating attributes. Pawlak's papers discuss its application in the domain of medical diagnostic systems. Here we apply it to the design of information retrieval systems accessing collections of documents. Advantages offered by the theory are: the implicit inclusion of Boolean logic; term weighting; and the ability to rank retrieved documents. In the first section we describe the theory. This is derived from the work by [PAWLAK84, PAWLAK82] and includes only the most relevant aspects of the theory. In the second we apply it to information retrieval. Specifically, we design the approximation space, search strategies as well as illustrate the application of relevance feedback to improve document indexing. Following this in section three we compare the rough set formalism to the Boolean, vector and fuzzy models of information retrieval. Finally we present a small scale evaluation of rough sets which indicates its potential in information retrieval.

Verification of morphological table based on probabilistic rough sets approach

Abstract: The objective of this paper is to propose two morphological table verification techniques, both based on the probabilistic rough sets approach. Fundamental aspects of the theories of rough sets and probabilistic approximate classification are discussed which have been adapted for the analysis of a morphological table. These two related theories were used to develop experimental computer programs with the ability to learn from examples, for the analysis of dependencies between variables in a given morphological table. The developed verification programs were used for analysis of two morphological tables of different complexity and development histories. The first test was for a nine-variable table, which has been under development for approximately 15 years and has undergone many changes and corrections. The other test was performed for a 42-variable table developed only recently. The diagrams prepared reveal the learning character of the verification process and the differences between learning b...

Approximate classification and rough sets

Abstract: W: R. Wille, editor, Klassifikation und Ordnung, pages 186-190. Tagungsband, Frankfurt/Main, 1988

Fuzzy Inductive Learning Based On Rough Sets

Abstract: Based on Rough Sets, the definable measure of fuzzy concepts in learning spaces is discussed, a constructive operation of learning space which can improve the inductive learning quality is presented and finally a fuzzy inductive learning algorithm called IDLP which may be used to automatically knowledge base building for expert systems is given.