# Efficient Rule Generation for Associative Classification
## 2 Related Work  
In the past, AC-based algorithms have been proposed and studied. The study's objective is to understand some drawbacks and to increase the effectiveness of the algorithms. [PERSON] et al. [5] introduced the CBA algorithm which integrated association rule mining and classification. The process of the CBA algorithm is divided into two steps. First, CARs are generated based on the famous search method the Apriori algorithm [20]. Second, CARs are sorted and then pruned to select efficient CARs in a classifier. The CBA algorithm was proven to produce a lower error rate than C4.5 [21]. Unfortunately, the CBA algorithm encounters a large number of candidate generation problems due to Apriori inheritance which finds all possible frequent rules at each level. [PERSON] et al. [22] presented the Classification based on Multiple Association Rules (CMAR) algorithm. Unlike CBA, CMAR adopts a Frequent pattern tree (FP-tree) and a Cosine R-tree (CR-tree) for rule generation and classification phases. It divides the subset in FP-tree to search frequent ruleitems and then adds the frequent ruleitems to CR-tree according to their frequencies. Hence, CMAR only needs to scan the database once. The CMAR algorithm uses multiple rules to predict unseen instances based on chi-square method. In the experiment, CMAR was compared with CBA and C4.5 in terms of accuracy. The experimental result shows that CMAR performs better than the others. [PERSON] [6] proposed an Enhanced Multi-label Classifier-based Associative Classification (eMCAC) for phishing website detection. It generates rules with multiple class labels from a single dataset without recursive learning. The eMCAC algorithm applies a vertical data format to represent datasets. The support and confidence values for a multi-label rule are calculated based on the average support and confidence values of all classes. The class is assigned to the test instance if attribute values are fully matched to the rule's antecedent. The experimental results show that the eMCAC algorithm outperforms CBA, PART, C4.5, jRiP, and MCAR [23] on the real-world phishing data in terms of accuracy. [PERSON] et al. [11] proposed the FACA algorithm for phishing website detection. It applies a Diffset [24] in the rule-generation process to increase the speed of classifier building time. First, the FACA algorithm discovers k-ruleitems by extending frequent (k \(-\) 1)-ruleitems. Then, ruleitems are ranked according to the number of attribute values, confidence, support, and occurrence. To predict unseen data, the FACA algorithm utilizes the All Exact Match Prediction Method. The method matches unseen data with all CARs in the classifiers. Next, unseen data are assigned to the class label with the highest count. From the experimental result, the FACA algorithm outperforms CBA, CMAR, MCAR, and ECAR [25] in terms of accuracy. [PERSON] and [PERSON] [15] introduced Predictability-Based Collective Class Association Rule algorithm (PCAR) to enhance rule evaluation. The PCAR algorithm uses inner cross-validation between the test dataset and train dataset to calculate a predictability value of CARs. Then, CARs are ranked according to rule predictive values, rule confidence, rule support, rule antecedent length, and rule occurrences. Finally, the full-matching method is applied to assign a class label for unseen data. To evaluate the performance of PCAR, PCAR was compared with C4.5, RIPPER, CBA, and MCAR on the accuracy, and PCAR was shown to outperform the others. [PERSON] et al. [16] proposed the WCBA algorithm to enhance the accuracy of a classifier based on the weighting technique. WCBA assumes that the importance of attributes is not equal. For example, in medicine, some attributes are more important than other attributes for prediction. Consequently, weights of all attributes are assigned by experts in the domain. Then, the weighted method is used to select useful CARs and a statistical measure is used for the pruning process.