ZENGDA GUAN
ABSTRACT
Quantitative associative classification (QAC) is favored for its strong explainability and satisfying predictive ability. However, existing QAC methods can't directly classify quantitative dataset basically, instead they work by partitioning quantitative data into a number of categories and then classify them, which cause information loss. Here, we propose a new QAC method which can directly estimate the probability of association rules using kernel mean embedding technology so that it can avoid the biases induced by partitioning and obtain competitive classification accuracy. Specifically, we implement an Apriori-like association rule discovery process and estimate the posterior probability of data by Bayes' rule with the terms being computed in a kernel mean embedding pattern. To verify our method, we take experiments on several data sets, and the results demonstrate that the method performs better than SVM, decision tree and partitioning Apriori algorithm. We also test the effect of the method's super-parameters including minimal support, minimal confidence and maximal attribute set size, and give an example of quantitative association rule set.
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