ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00028.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

Vagueness and uncertainty have attracted the attention of philosophers and logicians for many years. Recently, AI researchers contributed essentially to this area of research. Fuzzy set theory and the theory of evidence are seemingly the most appealing topics. On this note we present a new approach, based on the rough set theory, for looking to these problems. The theory of rough sets seems a suitable mathematical tool for dealing with problems of vagueness and uncertainty. This paper is a modified version of the author's lecture titled “An inquiry into vagueness and uncertainty,” which was delivered at the AI Conference in Wigry (Poland), 1

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00029.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain.There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational database. In addition, applications inherently “rough” could be more easily implemented and maintained in the rough relational datab

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00030.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

The Galois (or concept) lattice produced from a binary relation has proved useful for many applications. Building the Galois lattice can be considered a conceptual clustering method because it results in a concept hierarchy. This article presents incremental algorithms for updating the Galois lattice and corresponding graph, resulting in an incremental concept formation method. Different strategies are considered based on a characterization of the modifications implied by such an update. Results of empirical tests are given in order to compare the performance of the incremental algorithms to three other batch algorithms. Surprisingly, when the total time for incremental generation is used, the simplest and less efficient variant of the incremental algorithms outperforms the batch algorithms in most cases. When only the incremental update time is used, the incremental algorithm outperforms all the batch algorithms. Empirical evidence shows that, on the average, the incremental update is done in time proportional to the number of instances previously treated. Although the worst case is exponential, when there is a fixed upper bound on the number of features related to an instance, which is usually the case in practical applications, the worst‐case analysis of the algorithm also shows linear growth with respect to the number of instance

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00031.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real‐life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge base

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00032.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

We propose a procedure for estimating DBLEARN's potential for knowledge discovery, given a relational database and concept hierarchies. This procedure is most useful for evaluating alternative concept hierarchies for the same database. The DBLEARN knowledge discovery program uses an attribute‐oriented inductive‐inference method to discover potentially significant high‐level relationships in a database. A concept forest, with at most one concept hierarchy for each attribute, defines the possible generalizations that DBLEARN can make for a database. The potential for discovery in a database is estimated by examining the complexity of the corresponding concept forest. Two heuristic measures are defined based on the number, depth, and height of the interior nodes. Higher values for these measures indicate more complex concept forests and arguably more potential for discovery. Experimental results using a variety of concept forests and four commercial databases show that in practice both measures permit quite reliable decisions to be made; thus, the simplest may be most approp

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00033.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

In order to provide a background for rough set modeling of uncertainty, two types of incompleteness of information are discussed. Representation of uncertain knowledge acquired from incomplete information is outlined within the framework of information logics. Relational proof theory for the information logics is presented. It is shown how these logics and their proof systems can be handled in the GLEFATINF (Graphical&Logical Editing Framework) system. This computer program is a key component of the inference laboratory Atelier d'Inféence (ATINF) developed at LIFIA‐IMAG, our lab. It provides a general framework, independent of logic and proof systems, for combining inference tools, editing, and checking proofs. The basic principles of its design and implementation are given and its capabilities are discussed. Its application to define the information logics and their proof systems and to present proofs in these proof systems is discussed and illustrat

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00034.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge‐based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute‐oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine‐learning paradigm, especially learning‐from‐examples techniques, with rough set techniques. An attribute‐oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause‐effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute‐oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in d

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00035.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

This paper presents an empirical study of the use of the rough set approach to reduction of data for a neural network classifying objects described by quantitative and qualitative attributes. Two kinds of reduction are considered: reduction of the set of attributes and reduction of the domains of attributes. Computational tests were performed with five data sets having different character, for original and two reduced representations of data. The learning time acceleration due to data reduction is up to 4.72 times. The resulting increase of misclassification error does not exceed 11.06%. These promising results let us claim that the rough set approach is a useful tool for preprocessing of data for neural networks.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00036.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY

摘要:

The inverted pendulum poses serious problems for qualitative modeling methods, so it is a good benchmark to lest their performance. This paper shows how a new data analysis method known as rough set theory can be utilized to swing up and stabilize the pendulum. Our approach to this task consists of deriving control rules from the actions of a human operator stabilizing the pendulum and subsequently using them for automatic control. Rule derivation is based on the “learning from examples” principle and does not require knowledge of a quantitative model of the sys

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1995.tb00037.x

出版商:Blackwell Publishing Ltd    

年代:1995

数据来源: WILEY