# Coverage-Based Classification Using Association Rule Mining +
## 7 Discussion  
J&B produces classifiers that have on average far fewer rules than those produced by the \"classical\" rule-learning methods and slightly smaller number of rules comparing to associative classification models included in the comparison. J&B generated a reasonably smaller number of rules on bigger datasets compared to all other classification methods. J&B achieved the best accuracy on \"Breast cancer\", \"Hayes-root\" and \"Connect4\" datasets among all methods (80.5%, 79.3% and 81.2% respectively), while it got a slightly worse result on \"Nursery\" and \"Car.Evn\" datasets. On other datasets, J&B achieved almost similar results with all rule-based classification methods.  
We used the \"WEKA\" software to generate class association rules and to explore other classification algorithms because \"WEKA\" is open-source software that includes all the necessary classification approaches and it is relatively easy to use and program. Our proposed algorithm has some limitations:  
* To get \"enough\" class association rules (\"enough\" means at least 5-10 rules for each class value--this situation mainly happens with imbalanced datasets) for each class value and achieve a reasonable overall coverage, we need to apply appropriate minimum support and minimum confidence thresholds. That is, we should take into consideration the class distribution of each dataset.
* In this research work, one of the novel approaches is to apply the intended coverage constraint which becomes the stopping criteria of selecting rules for our classifier. Here, we aimed to show that it is possible to achieve reasonably high accuracy without covering the whole dataset (especially when the dataset has some missing or noisy examples). This criterion becomes an advantage of our method that produces significantly smaller classification rules on bigger datasets.
* The J&B produced an accurate and compact classifier on a dataset which has a large number of attributes and records (especially, with balanced datasets on class-distribution), while the  
Figure 3: Comparison of the J&B classifier on Accuracy, Precision, Recall and F-measure.  
performance of J&B will be slightly worse with the datasets having a small number of attributes and a large number of records (as proven on \"Nursery\" and \"Car.Evn\" datasets).
* It is unnecessary to remove the missing values from the dataset because all \"classical\" classification algorithms and our method can handle missing values directly.
* J&B generates a smaller number of classification rules compared to all other traditional and associative classifiers on bigger datasets.
* We tried to solve the outer-class overlapping problem (that means some samples from different classes have very similar characteristics) by grouping the CARs according to their class value, and the inter-class overlapping problem (several rules that belong to the same class may cover the same samples) by selecting the rules based on database coverage.
* Our method may get slightly worse results in terms of classification accuracy than other traditional classification methods on imbalanced datasets, because unbalanced class distribution may affect the rule-generation part of our algorithm.
* The time complexity of the APRIORI algorithm is potentially exponential but, on the other hand, we gain in more exhaustively exploring the entire search space and in part, this issue can be solved by trying to use more efficient data representation and parallelization of the algorithm, which is what we plan to do in future work.