Many studies have shown that rule-based classification algorithms perform well in classifying categorical and sparse high-dimensional databases. However, a fundamental limitation with many rule-based classifiers is that they find the classification rules in a coarse-grained manner. They usually use heuristic methods to prune the search space, and select the rules based on the sequential database covering paradigm. Thus, the so-mined rules may not be the globally best rules for some instances in the training database. To make worse, these algorithms fail to fully exploit some more effective search space pruning methods in order to scale to large databases.
In this paper we propose a new classifier, HARMONY, which directly mines the final set of classification rules. HARMONY uses an instance-centric rule-generation approach in the sense that it can assure for each training instance, one of the highest-confidence rules covering this instance is included in the result set, which helps a lot in achieving high classification accuracy. By introducing several novel search strategies and pruning methods into the traditional frequent itemset mining framework, HARMONY also has high efficiency and good scalability. Our thorough performance study with some large text and categorical databases has shown that HARMONY outperforms many well-known classifiers in terms of both accuracy and efficiency, and scales well w.r.t. the database size.
Wang, Jianyong; Karypis, George.
HARMONY: Efficiently Mining the Best Rules for Classification.
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