Discriminant versus rough sets approach to vague data analysis

Abstract. The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory -nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many real-life applications of the theory have proved its practical usefulness. The paper presents the basic assumptions underlying the rough sets philosophy, gives its fundamental concepts and discusses briefly some areas of applications, in particular in decision support. Finally further problems are shortly outlined.

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“…Furthermore, some relations exist between the rough set theory and discriminant analysis [3] and the Boolean reasoning methods [11].…”

Section: The Relationship To Other Theoriesmentioning

confidence: 99%

Rough set approach to knowledge-based decision support

Pawlak

1997

European Journal of Operational Research

581

245

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“…In case of rough sets we can use more heuristic approaches, e.g. connected with dynamic reducts [16] or dividing the set of attributes into some disjoint subsystems and analysing the significance of attributes inside these subsystems (see [18,28]) or an idea of using, so-called, data templates [16].…”

Section: Methodsmentioning

confidence: 99%

“…This allowed us to select the subset of attributes. The methodology used in this part of the analysis followed the general methodological schema presented in [20,18,24,27].…”

Section: Final Remarksmentioning

confidence: 99%

Rough Set Theory and Decision Rules in Data Analysis of Breast Cancer Patients

Załuski

Szoszkiewicz

Krysiński

et al. 2004

Transactions on Rough Sets I

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Abstract.In this paper an approach based on the rough set theory and induction of decision rules is applied to analyse relationships between condition attributes describing breast cancer patients and their treatment results. The data set contains 228 breast cancer patients described by 16 attributes and is divided into two classes: the 1st class -patients who had not experienced cancer recurrence; the 2nd class -patients who had cancer recurrence. In the first phase of the analysis, the rough sets based approach is applied to determine attribute importance for the patients' classification. The set of selected attributes, which ensured high quality of the classification, was obtained. Then, the decision rules were generated by means of the algorithm inducting the minimal cover of the learning examples. The usefulness of these rules for predicting therapy results was evaluated by means of the cross-validation technique. Moreover, the syntax of selected rules was interpreted by physicians. Proceeding in this way, they formulated some indications, which may be helpful in making decisions referring to the treatment of breast cancer patients. To sum up, this paper presents a case study of applying rough sets theory to analyse medical data.

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“…Exhaustive information can be found in (Pawlak, Z. 1982;or Fibak, J. et al, 1986;Slowinski, K., et al 1988;Krusinska, E. et al, 1992;Slowinski, R. and Stefanowski, J., 1989).…”

Section: The Rough Sets Approachmentioning

confidence: 99%