# Coverage-Based Classification Using Association Rule Mining +
## 2 Related Works
Specifically, in the rule-selecting process, when a rule is being evaluated to be a potential candidate for a classifier, ranking of the remaining untested rules is also updated constantly after removing the training examples covered by that rule. The most important difference between our method and all other similar AC methods is that our approach has the stopping criteria in the rule-evaluation step based on user-defined coverage (for the learning dataset). In the associative classification method [47], researchers proposed a new measure, called \"condenseness\" to evaluate the infrequent rule items. Authors argued that infrequent rule items which are not satisfied with the minimum support threshold could produce strong association rules for classification. \"Condenseness\" of infrequent rule items is the average of _lift_ of all association rules that can be generated from those rule items. A rule item with a high condenseness means that its items are closely related and can produce potential rules for AC, although it does not have enough support. A new associative classifier, CARC--Condensed Association Rules for Classification, is presented based on the concept of \"condenseness\". Association rules are generated by using the modified \"APRIORI\" algorithm and develop the new strategy of rule inference with the condenseness. A recently proposed associative classifier called ARCID (Association Rule-based Classification for Imbalanced Datasets) aims to extract hidden regularities and knowledge from imbalanced datasets by emphasizing information extracted from minor classes. The key difference between our method and ARCID is in the rule-selection process. ARCID selects the best \(k\) rules by using the following interestingness measures: first, rules that have higher accuracy (rules that have higher supports can have higher predictive accuracy) and belong to major classes; second, rare rules that have lower support and belong to minor classes, while our algorithm selects the best rules according to their overall coverage (training dataset coverage) and the number of rules generated by our model depends on the _intended_overall_coverage_ threshold. Another recent approach [48] aims to produce an optimized associative classifier based on non-trivial data insertion for the incremental dataset. To solve the unattended data insertion and optimization problem, a new technique called \"C-NTDI\" is used to build a classifier, when there is an insertion of data that takes place in a non-trivial fashion in the initial data that are used for updating the classification rules. In the rule-generation part, PPCE and BAT [49] techniques are applied: PPCE is used to generate the rules, and further Proportion of Frequency Occurrence check with BAT Algorithm (PFOCBA) is applied for pruning the rules that are generated. The FURIA (Fuzzy Unordered Rule Induction Algorithm) is a rule-based classification method [42] which is a modified and extended version of the RIPPER algorithm [23]. FURIA learns fuzzy rules instead of conventional rules and unordered rulesets (namely a set of rules for each class in a one-vs-rest scheme) instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. The idea is to generalize the existing rules until they cover the example.