Polkowski, L., Skowron, A., Eds.: Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, 1998. Home/Search Document Not in Database Summary Related Articles Check |
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Intelligent Data Analysis in Medicine - Lavrac, Keravnou, Zupan (2000) (1 citation) (Correct)
....generated. Good results have been obtained with dynamic reducts [159] that use a combination of reduct computation and statistical resampling. Within the RS paradigm new approaches to discretization, feature selection, 33 symbolic attribute grouping, etc. have been designed (for references, see [126, 127, 128]) There exist several software tools for RS, e.g. the Rosetta system [139] The list of applications of RS in medicine is significant. It includes extracting diagnostic rules, image analysis and classification of histological pictures, modeling set residuals, EEG signal analysis, etc. Examples ....
....is significant. It includes extracting diagnostic rules, image analysis and classification of histological pictures, modeling set residuals, EEG signal analysis, etc. Examples of RS analysis in medicine include [53, 80, 166] For an up todate reference that includes medical applications, see [127, 128, 99]. 3.1.3 Association rules The problem of discovering association rules has recently received much attention in the data mining community. The problem of inducing association rules [2] is defined as follows: Given a set of transactions, where each transaction is a set of items (i.e. literals of ....
Polkowski, L. and Skowron, A., eds., Rough Sets in Knowledge Discovery 1: Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing. Physica-Verlag, 1998.
Empirical Distribution of Non-Precise Data: Roughness and.. - Slavka Bodjanova Texas (Correct)
....by a pair of precise concepts called the lower and the upper approximations. Rough set theory is based on equivalence relations describing partitions made of classes of indiscernible objects. This new approach proved to be useful in many applications. For reference see, e.g. Ziarko (1994) or Polkowski and Skowron (1998). Several studies have already been conducted about combinations of rough and fuzzy sets. The notions of rough fuzzy sets and fuzzy rough sets were introduced by several researchers, among them, e.g. Dubois and Prade (1990) or Kuchneva (1992) Suppose that a finite number of non precise ....
Polkowski, L. and Skowron, A. (1998). Rough Sets in Knowledge Discovery: Methodology and Applications. Physica-Veralg. Heidelberg.
Rough Set Rudiments - Pawlak, Skowron (1996) (1 citation) Self-citation (Skowron) (Correct)
.... respect to B) Several generalizations of the classical rough set approach based on approximation spaces defined by (U; R) where R is an equivalence relation (called indiscernibility relation) in U , have been reported in the literature (for references see the papers and bibliography in [PaS] [PS1], PS2] Let us mention two of them. A generalized approximation space can be defined by AS = U; I ; where I is the uncertainty function defined on U with values in the powerset P (U) of U (I(x) is the neighboorhood of x) and is the inclusion function defined on the Cartesian product P (U) ....
....nonconflicting) rules. Decision tables containing inconsistent decision rules are called inconsistent (nondeterminis tic, conflicting) otherwise the table is consistent (de terministic, nonconflicting) Numerous methods have been developed for different decision rule generation (see, e.g. [PaS, PS1, PS2, PS3]) When a set of rules have been induced from a decision table containing a set of training examples, they can be inspected to see if they reveal any novel relationships between attributes that are worth pursuing for further research. Furthermore, the rules can be applied to a set of unseen cases ....
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Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 1: Methodology and Applications. Physica-Verlag, Heidelberg (1998)
On Decomposition for Incomplete Data - Latkowski (2003) (Correct)
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Polkowski, L., Skowron, A., Eds.: Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, 1998.
On Decomposition for Incomplete Data - Latkowski (2002) (Correct)
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L. Polkowski and A. Skowron, editors. Rough Sets in Knowledge Discovery 1: Methodology and Applications. Physica-Verlag, 1998.
Reasoning about Knowledge using Rough Sets - Weiru Liu School (Correct)
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Polkowski, L. and Skowron, A., (ed.) (1998a) Rough Sets in Knowledge Discovery 1: methodology and applications. In Studies in Fuzziness and Soft Computing Vol
Uncertainty Management - Berztiss (Correct)
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L. Polkowski and A. Skowron #eds.#, Rough Sets in Knowledge Discovery 1: Methodology and Applications #Physica-Verlag, 1998#.
An Overview of Rough Set Semantics for Modal and Quantifier Logics - Liau (2000) (Correct)
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L. Polkowski and A. Skowron, editors. Rough Sets in Knowledge Discovery 1: Methodology and Applications, Volume 18 of Studies of Fuzziness and Soft Computing. Physica-Verlag, Heidelberg, Germany, 1998.
Rough Sets: A Knowledge Discovery Technique for Multifactorial .. - Øhrn, Rowland (Correct)
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Lech Polkowski and Andrzej Skowron, editors. Rough Sets in Knowledge Discovery 1: Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing. Physica-Verlag, Heidelberg, Germany, 1998.
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