Hui Wang, Ivo Dntsch, and Gnther Gediga. Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111--136, 2000.  Home/Search Document Not in Database Summary Related Articles Check |
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Hyperrelations in Version Space - Wang, Düntsch, al. (2004) Self-citation (Wang Dntsch Gediga) (Correct)
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Hui Wang, Ivo Dntsch, and Gnther Gediga. Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111--136, 2000.
A Density Based Approach to Classification - Wang, Bell, Düntsch (2003) Self-citation (Wang) (Correct)
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H. Wang, I. Duntsch, and G. Gediga. Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111--136, 2000.
Classification Based on Contextual Probability - Hui Wang School (2003) Self-citation (Wang) (Correct)
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Hui Wang, Ivo Duntsch, and Gunther Gediga. Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111-- 136, 2000.
Maximum Consistency of Incomplete Data Via Non-invasive.. - Gediga, Düntsch (2002) Self-citation (Dntsch Gediga) (Correct)
.... non invasive tools which do not make any distributional or other strong assumptions: Dependency rules in the spirit of rough set data analysis (RSDA) 15] statistical significance of symbolic rules by randomisation methods [5] data discretisation by using only classification information ( 6] [19]) and model selection by entropy minimisation [7] In this paper we describe an algorithm to impute missing values from given data alone, and analyse its performance. Our approach is based on non numeric rule based data analysis. In contrast to statistical procedures, such analysis offers no ....
Wang, H., Dntsch, I., and Gediga, G. (2000). Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111--136. 13
Rough Set Data Analysis - Düntsch (2000) (1 citation) Self-citation (Dntsch Gediga) (Correct)
....sepal length values 43 48 and 53 are collected into one common value 46; in this attribute, filtering reduces the number of classes from 35 to 22. Observe the large reduction in the number of classes of the petal attributes. Extensions of this method to multi attribute filtering can be found in [79, 80]. 9 Extensions and variations of RSDA In this section we will briefly describe other directions into which RSDA has branched, some of which are only beginning to be investigated. The variable precision rough set model (VPRS) introduced in [83] is a generalisation of the original RSDA in the ....
Wang, H., Dntsch, I. & Gediga, G. (1998b). Classificatory filtering in decision systems. Submitted for publication.
Logical and algebraic Techniques for Rough Set Data Analysis - Düntsch, Gediga Self-citation (Dntsch Gediga) (Correct)
....interpreted multi valued system. Therefore, we will always interpret multi valued information systems disjunctively in the sense of (9) this will enable us to capture rules of the form (3) This type of information system has also recently been used for data filtering and compression [20, 73]. A detailed investigation of the rule systems associated with multi valued information systems is the forthcoming [22] 3 Deterministic reasoning In this Section, we are concerned with deterministic rules of type (1) This has been studied, among others, in [45, 46, 67] We will show that ....
Wang, H., Dntsch, I. & Gediga, G. (1998). Classificatory filtering in decision systems. Submitted for publication.
Lattice Machine: Version Space in Hyperrelations.. - Wang, Düntsch.. (2002) (Correct)
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Hui Wang, Ivo Duntsch, and Gunther Gediga. Classificatory filtering in decision systems. International Journal of Approximate Reasoning, 23:111--136, 2000.
Applying Rough Sets to Market Timing Decisions - Lixiang Shen Han (Correct)
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H. Wang, I. Du ntsch, G. Gediga, Classificatory filtering in decision systems, International Journal of Approximate Reasoning 23 (2000) 111 -- 136.
Structure Identification of Fuzzy Classifiers - Abonyi, Roubos (2000) (Correct)
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H. Wang and I. Dntsch and G. Gedinga. (2000) Classificatory filtering in decision systems. Int. J. Approximate Reasoning 23, 111-136.
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