Maimon O. and Rokach L., "Data Mining by Attribute Decomposition with semiconductors manufacturing case study", In "Data Mining for Design and Manufacturing: Methods and Applications", Kluwer Academic Publishers, pp. 311-336, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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Pattern Analysis and Applications manuscript No. - Will Be Inserted Self-citation (Rokach) (Correct)
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Maimon O. and Rokach L., "Data Mining by Attribute Decomposition with semiconductors manufacturing case study", In "Data Mining for Design and Manufacturing: Methods and Applications", Kluwer Academic Publishers, pp. 311-336, 2001.
Improving Supervised Learning by Feature Decomposition - Maimon, Rokach Self-citation (Maimon Rokach) (Correct)
....hand DOT was more accurate than Naive Bayes and IFN in 8 databases, and 7 databases respectively. Furthermore DOT was significantly more accurate than C4.5 in 9 databases, and less accurate in only 2 databases. The contribution of feature decomposition in real life applications can be found in [27]. 10 The link between Error Reduction and the Problem Complexity. In order to understand when the suggested approach introduces considerable performance enhancement, we checked the correlation between error reduction and the problem complexity. There are two obvious alternatives for measuring ....
Maimon, O. and Rokach, L., "Data Mining by Attribute Decomposition with semiconductors manufacturing case study" in D. Bracha, Editor, Data Mining for Design and Manufacturing: Methods and Applications, Kluwer Academic Publishers, 2001.
Theory and Applications of Attribute Decomposition - Department (2001) (1 citation) Self-citation (Maimon Rokach) (Correct)
....training sets, we can only estimate the probability and it is easier to approximate probabilities with lower dimensions. It can be shown that the number of possible decompositions (i.e. the size of the search space) increases in a strong exponential manner as the number of input attributes grows [14], i.e. an exhaustive search is not practical for high number of input attributes. Hereby we present three Lemmas that will shed light on the suggested problem. The proof of the following Lemmas is straightforward. Lemma 2 (Sufficient condition) Let Z be a decomposition, which satisfies G k ; k = ....
O. Maimon and L. Rokach. Data Mining by Attribute Decomposition with semiconductors manufacturing case study in D. Bracha, Editor, Data Mining for Design and Manufacturing: Methods and Applications, Kluwer Academic Publishers, 2001.
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