A Study on the Performance of Large Bayes Classifier
نویسندگان
چکیده
Large Bayes (LB) is a recently introduced classifier built from frequent and interesting itemsets. LB uses itemsets to create context-specific probabilistic models of the data and estimate the conditional probability P(c i |A) of each class c i given a case A. In this paper we use chi-square tests to address several drawbacks of the originally proposed interestingness metric, namely: (i) the inability to capture certain really interesting patterns, (ii) the need for a userdefined and data dependent interestingness threshold, and (iii) the need to set a minimum support threshold. We also introduce some pruning criteria which allow for a trade-off between complexity and speed on one side and classification accuracy on the other. Our experimental results show that the modified LB outperforms the original LB, Naïve Bayes, C4.5 and TAN.
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