Comparison of the Rough Sets Approach and Probabilistic Data Analysis Techniques on a Common Set of Medical Data

Krusińska, Ewa; Babić, Ankica; Słowiński, Roman; Stefanowski, Jerzy

doi:10.1007/978-94-015-7975-9_16

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…A problem similar to casualness of attributes, namely, the reliability of rules, was addressed by Krusin´ska et al (1992a). The authors define an index which they call strength of a rule by counting the number of objects a rule refers to.…”

Section: Casual Dependenciesmentioning

confidence: 99%

“…Rough set analysis, an emerging technology in artificial intelligence (Pawlak, Grzymała-Busse, Słowin´ski & Ziarko, 1995), has been compared with statistical models, see for example, Wong, Ziarko and Ye (1986), Krusin´ska, Babic, Słowin´ski and Stefanowski (1992a) or Krusin´ska, Słowin´ski and Stefanowski (1992b). One area of application of rough set theory is the extraction of rules from databases; these rules then are sometimes claimed to be useful for future decision making or prediction of events.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical evaluation of rough set dependency analysis

Düntsch¹,

Gediga²

1997

International Journal of Human-Computer Studies

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Rough set data analysis (RSDA) has recently become a frequently studied symbolic method in data mining. Among other things, it is being used for the extraction of rules from databases; it is, however, not clear from within the methods of rough set analysis, whether the extracted rules are valid.In this paper, we suggest to enhance RSDA by two simple statistical procedures, both based on randomization techniques, to evaluate the validity of prediction based on the approximation quality of attributes of rough set dependency analysis. The first procedure tests the casualness of a prediction to ensure that the prediction is not based on only a few (casual) observations. The second procedure tests the conditional casualness of an attribute within a prediction rule.The procedures are applied to three data sets, originally published in the context of rough set analysis. We argue that several claims of these analyses need to be modified because of lacking validity, and that other possibly significant results were overlooked.1997 Academic Press Limited I. DU NTSCH AND G. GEDIGA

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Section: Casual Dependenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical evaluation of rough set dependency analysis

Düntsch¹,

Gediga²

1997

International Journal of Human-Computer Studies

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“…The indiscernibility of objects prevents the precise classification of objects. The rough sets approach was previously compared with discriminant analysis and tree c!assifiers techniques (see Krusinska et al (1992aKrusinska et al ( ), (1992bKrusinska et al ( ), (1993). Lower and upper approximations of a set allow to define a coefficient called quality of classification.…”

Section: Rough Sets Approachmentioning

confidence: 99%

New Approaches in Classification and Data Analysis

Schader¹,

Diday²,

Lechevallier³

et al. 1994

Studies in Classification, Data Analysis, and Knowledge Organization

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“…These rules represent not only the relationships between the description of objects by attributes and their assignment to particular classes, but can also be used for the classification of new objects (Krusinska et al, 1992). Another advantage of these two inductive methods is that one can eliminate all superfluous attributes in order to find the most significant attributes for the classification.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, inductive methods like decision trees (Marais, Patell and Wolfson, 1984;Srinivasan and Kim, 1987;Cronan, Glorfield and Perry, 1991;Piramuthu, Shaw and Gentry, 1994;Tessmer, 1997;Joos et al, 1998) or rough sets have better knowledge representational structure in the sense that they can be used to derive decision rules. These rules represent not only the relationships between the description of objects by attributes and their assignment to particular classes, but can also be used for the classification of new objects (Krusinska et al, 1992). Another advantage of these two inductive methods is that one can eliminate all superfluous attributes in order to find the most significant attributes for the classification.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Rough Sets and Recursive Partitioning Induction Approaches: An Application to Commercial Loans

Daubie

Meskens

Levecq

2002

Int Trans Operational Res

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Credit scoring is the term used to describe methods utilized for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have a better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision trees have already been used for credit granting, the rough sets approach is rarely utilized in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. We show that decision trees obtain better results with 87.5% of good classifications with a pruned tree, against 76.7% for rough sets. However, decision trees make more type-II errors than rough sets, but fewer type-I errors.

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Comparison of the Rough Sets Approach and Probabilistic Data Analysis Techniques on a Common Set of Medical Data

Cited by 11 publications

References 9 publications

Statistical evaluation of rough set dependency analysis

Statistical evaluation of rough set dependency analysis

New Approaches in Classification and Data Analysis

A Comparison of the Rough Sets and Recursive Partitioning Induction Approaches: An Application to Commercial Loans

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