C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
ACME: an associative classifier based on maximum entropy principle
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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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(ci|A) of each class 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 user-defined 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.