O. Maimon, A. Kandel, and M. Last, Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases. In Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry, Eds. SpringerVerlag, London, 1999. Home/Search Document Details and Download
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Anytime Algorithm for Feature Selection - Mark Last Abraham Self-citation (Maimon Kandel Last) (Correct)
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O. Maimon, A. Kandel, and M. Last, Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases. In Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry, Eds. SpringerVerlag, London, 1999.
Information-Theoretic Algorithm for Feature Selection - Last, Kandel, Maimon Self-citation (Maimon Kandel Last) (Correct)
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O. Maimon, A. Kandel, and M. Last, Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases. In Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry, Eds. Springer-Verlag, London, pp. 315-326, 1999.
Knowledge Discovery in Time Series Databases - Last, Klein, Kandel (2001) (12 citations) Self-citation (Kandel Last) (Correct)
....predictive accuracy of the model . Fuzzify and reduce the set of extracted rules A. Information Theoretic Network Our approach to knowledge discovery in preprocessed time series is based on the information theoretic method of knowledge discovery and data mining, initially introduced by us in [12] and [13] The interactions between the input (predicting) attributes and the target (classification) attribute are modeled by an information theoretic connectionist network, which consists of the root node, a changeable number of hidden layers (one layer for each input attribute) and a target ....
....to the chain rule [1] the mutual information between the input attributes and the target (defined as the overall decrease in the conditional entropy) is equal to the sum of drops in conditional entropy at all the hidden layers. Detailed descriptions of the algorithm steps are provided in [12] and [13] In Fig. 1, a example of a two layered network (based on two selected input attributes) is shown. The first input attribute has three values, represented by nodes no. 1, 2, and 3 in the first layer, but only nodes no. 1 and 3 are split due to the statistical significance test. The ....
O. Maimon, A. Kandel, and M. Last, "Information---theoretic fuzzy approach to knowledge discovery in databases," in Advances in Soft Computing ---Engineering Design and Manufacturing, R. Roy, T. Furuhashi, and P. K. Chawdhry, Eds. London, U.K.: Springer-Verlag, 1999, pp. 315--326.
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