Optimization of the Bayesian classifier's structure using GMDH algorithm to forecast Internetclients' preferences. Zhyliaev Sergey, Ryabokon Dmitriy. IWIM, Prague, 2007.
Article (in pdf)
Abstract. The paper describes an algorithm allowing to raise accuracy of the Bayesian classifier due to optimization of its structure using the GMDH algorithm MULTI. Comparison of accuracy of proposed algorithm vs. regression algorithms is made on the basis of forecasting Internet-clients’ preferences. The specific character of this problem lies in the fact that Internet-consumers have a number of attributes. Developed algorithm essentially differs from the Bayesian classifier in that it allows to find out few “strong”, statistically stable attributes among large set of properties characterizing object and thus to increase the accuracy of classification.
Keywords. Bayesian classifier, structure optimization, regression, GMDH
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