# Coverage-Based Classification Using Association Rule Mining +
## 2 Related Works  
In computer science literature, there are a lot of rule-based, tree-based and probabilistic classification models proposed by prominent researchers. Since we propose associative classification methods based on database coverage, we discuss the performed research works in associative classification fields that were related to our proposed approaches in this section. CMAR, an associative classification method, also uses multiple association rules for classification [8]. This method first extends FP-growth (an efficient frequent pattern mining) to mine large datasets [45]. Next, CMAR employs a novel data structure to improve the overall accuracy and efficiency, called CR-tree. The main goal of using CR-tree is to store and retrieve a large number of rules compactly and efficiently. In the rule-selection process, CMAR extracts the highly confident, highly related rules based on database coverage and analyzes the correlation between those rules. More precisely, for each rule \(R\), all the examples are found covered by rule \(R\) and if \(R\) correctly classifies at least one example, then \(R\) will be included to a final classifier and cover count (cover count threshold \(C\) is applied at the beginning) of those examples covered by R is increased by 1. If the cover count of an example is exceeded by \(C\), then that example is removed. This process continues while both the training dataset and ruleset are not empty. The key differences are that our approach uses the APRIORI algorithm [3] to generate frequent itemsets, instead of FP-tree and rules being pruned based on minimum support and confidence constraints. In CBA and MCAR approaches that use the so-called vertical mining approach, the \"APRIORI\" algorithm is executed first to generate CARs. Then, both algorithms apply greedy pruning based on database coverage to produce predictive rules. They have different rule-selection methods compared to ours--the rule that can classify at least one training example, that is, if the candidate rule's body and class labels match the training example's, the body and class labels are selected for the classifier. This step is different in our method since we are trying to increase the overall coverage and accuracy as well as decreasing the size of the classifier: we select the rule as a potential candidate, only if the rule contributes to improving the overall coverage (if the rule's body matches the training example's body, we ignore the class label). The CPAR algorithm proposed by [PERSON] and [PERSON] [10] combines the advantages of associative classification and traditional rule-based classification. CPAR utilizes a greedy algorithm to generate rules directly from the training dataset, instead of generating large candidate rules from frequent itemsets as in other associative classification methods. To avoid overfitting, CPAR uses expected accuracy to evaluate each rule and the classification process is also different than traditional classification approaches: First, CPAR selects all rules whose bodies match the testing example, second, it extracts the best k rules for each class among the rules selected in step 1, and finally, CPAR compares the average expected accuracy of the best \(k\) rules for each class found in step 2 and predicts the class label which achieves the highest expected accuracy. The AC method proposed in Reference [46] is based on database coverage associated with test data. During the classification process of the test dataset, this approach considers the class label of rules that are similar to the test data and rule's position in the classifier. The score of each class label is computed based on the above conditions and the test data is predicted by the class label that has the highest score. Another AC [37] method aims to build a classifier based on rule-pruning. This method applies the vertical mining (TidList) to generate class association rules. Researchers proposed a new procedure called Active Rule Pruning (APR) to improve predictive accuracy and to reduce rule redundancy.