# Coverage-Based Classification Using Association Rule Mining +
## 5 Our Associative Classification Approach--J&B
### Overall Coverage and Accuracy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CARs** & **Support (\%)** & **Confidence (\%)** \\ \hline \(\{\)a1\(=\) 3, a3\(=\) 2, a4\(=\) 1, a5\(=\) 3\(\}\rightarrow\{\)1\(\}\) & 4 & 72 \\ \(\{\)a1\(=\) 1, a2\(=\) 5\(\}\rightarrow\{\)3\(\}\) & 5 & 100 \\ \(\{\)a1\(=\) 3, a4\(=\) 1\(\}\rightarrow\{\)1\(\}\) & 8 & 100 \\ \(\{\)a1\(=\) 1, a3\(=\) 5, a4\(=\) 4\(\}\rightarrow\{\)3\(\}\) & 5 & 100 \\ \(\{\)a1\(=\) 2, a2\(=\) 2, a3\(=\) 2\(\}\rightarrow\{\)2\(\}\) & 5 & 63 \\ \(\{\)a2\(=\) 3, a3\(=\) 1, a4\(=\) 4, a5\(=\) 2\(\}\rightarrow\{\)1\(\}\) & 3 & 80 \\ \(\{\)a1\(=\) 2, a2\(=\) 3, a3\(=\) 4\(\}\rightarrow\{\)2\(\}\) & 11 & 85 \\ \(\{\)a3\(=\) 1, a5\(=\) 2\(\}\rightarrow\{\)1\(\}\) & 8 & 90 \\ \(\{\)a1\(=\) 1, a2\(=\) 5, a5\(=\) 5\(=\)\(\}\rightarrow\{\)3\(\}\) & 6 & 65 \\ \(\{\)a1\(=\) 1, a3\(=\) 4, a5\(=\) 2\(\}\rightarrow\{\)2\(\}\) & 10 & 85 \\ \(\{\)a1\(=\) 1, a2\(=\) 2, a3\(=\) 1, a4\(=\) 3, a5\(=\) 1\(\}\rightarrow\{\)3\(\}\) & 4 & 64 \\ \(\{\)a2\(=\) 2, a5\(=\) 5\(\}\rightarrow\{\)2\(\}\) & 6 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Generated CARs from the training dataset \begin{table}
\begin{tabular}{c c c} \hline \hline
**CARs** & **Support (\%)** & **Confidence (\%)** \\ \hline \(\{\)a1\(=\) 3, a4\(=\) 1\(\}\rightarrow\{\)1\(\}\) & 8 & 100 \\ \(\{\)a2\(=\) 2, a5\(=\) 5\(\}\rightarrow\{\)2\(\}\) & 6 & 100 \\ \(\{\)a1\(=\) 1, a2\(=\) 5\(\}\rightarrow\{\)3\(\}\) & 5 & 100 \\ \(\{\)a1\(=\) 1, a3\(=\) 5, a4\(=\) 4\(\}\rightarrow\{\)3\(\}\) & 5 & 100 \\ \(\{\)a3\(=\) 1, a5\(=\) 2\(\}\rightarrow\{\)1\(\}\) & 8 & 90 \\ \(\{\)a1\(=\) 2, a2\(=\) 3, a3\(=\)4\(\}\rightarrow\{\)2\(\}\) & 11 & 85 \\ \(\{\)a1\(=\) 1, a3\(=\) 4, a5\(=\) 2\(\}\rightarrow\{\)2\(\}\) & 10 & 85 \\ \(\{\)a2\(=\) 3, a3\(=\) 1, a4\(=\) 4, a5\(=\) 2\(\}\rightarrow\{\)1\(\}\) & 3 & 80 \\ \(\{\)a1\(=\) 3, a3\(=\) 2, a4\(=\) 1, a5\(=\) 3\(\}\rightarrow\{\)1\(\}\) & 4 & 72 \\ \(\{\)a1\(=\) 1, a2\(=\) 2, a5\(=\) 5\(=\}\rightarrow\{\)3\(\}\) & 6 & 65 \\ \(\{\)a1\(=\) 1, a2\(=\) 2, a3\(=\) 1, a4\(=\) 3, a5\(=\) 1\(\}\rightarrow\{\)3\(\}\) & 4 & 64 \\ \(\{\)a1\(=\) 2, a2\(=\) 2, a3\(=\) 3\(=\) 2\(\}\rightarrow\{\)2\(\}\) & 5 & 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sorted class association rules. Our classifier includes 6 rules. In this example, intended coverage is equal to 80% and 6 classification rules in the classifier cover 80% of the learning set. Since our training dataset has some examples with missing values, our classifier covered the whole training dataset (examples without missing values). Other rules also may cover unclassified examples, but we cannot exceed the user-defined training dataset coverage threshold. This is our stopping criterion and we cannot include other rules into our classifier. We cannot also include classification rules which cover only classified examples (this means it does not contribute to the improvement of overall coverage). Now, we classify the following unseen example:  
\[\{\text{a1}=1,\text{a2}=5,\text{a3}=5,\text{a4}=4,\text{a5}=5\}\rightarrow?\]  
So, this example is classified by the third and fourth classification rules. The class values of the rules which correctly classified the new example are 3 and 3. So, our classifier predicts that the class value of the new example is 3 (because majority class value is 3).