# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 4 Methods and Materials
### Experimental Results Analysis  
The clustering accuracy for selected datasets is shown in Table 2. For each dataset, D\({}_{i}\) in the table displays 3 groups (rows) of results. Each row indicates results corresponding to a percentage of selected features, \(\pi=\frac{M}{b}\), is set to 0.2, 0.5, and 0.8. As shown in Table 2, there is at least one subset of features that has better clustering accuracy than the whole dataset. This indicates the existence of noisy features, which reduces the clustering accuracy of the whole dataset. If we remove these noisy features the performance will improve. For example, with D1, 9 out of 12 configurations using FS algorithms lead to higher clustering accuracy than using the full dataset. The best clustering accuracy for D1, 0.6613, is found when we select 0.5 of features by Spec Algorithm. The best clustering accuracy for D2 and D3 occurs when we select 0.2 of features by Spec Algorithm. We found that if we select 0.2 of features in D1, D2, D3, and D5, by Spec Algorithm, we will get better clustering accuracy than by applying the clustering algorithm to the whole dataset. Table 3 shows the NMI results for the selected 5 datasets. Similar to clustering accuracy, for all datasets, there is at least one subset of features which outperforms using the whole dataset in terms of NMI. This emphasizes the existence of noisy features that negatively affect the NMI accuracy of the whole dataset. For example, all subsets of features, recommended by all algorithms in D1, have NMI accuracy better than the whole dataset. The best NMI accuracies are found when we select 0.5 percentage of features by Spec Algorithm. Corresponding with clustering accuracy results, we find that if we select 0.2 of features in D1, D2, D3, and D5 by Spec Algorithm, we will get better NMI accuracy than by applying the clustering algorithm to whole datasets. The Spec algorithm has the best clustering-based performing measures for our five insurance datasets. Table 4 shows the KNN classification accuracy. Like clustering accuracy and KNN accuracy, for all datasets, there is at least one subset of features which outperforms the whole dataset in terms of KNN accuracy. This emphasizes the existence of noisy features that negatively affect the KNN accuracy of whole datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Per. of.**} & \multicolumn{2}{c}{**No. Sel.**} & \multirow{2}{*}{**GFSA**} & \multirow{2}{*}{**LS**} & \multirow{2}{*}{**Specu**} & \multirow{2}{*}{**USFSM**} & \multicolumn{2}{c}{**All-Features**} \\ \cline{3-3} \cline{6-9}  & 0.2 & 4 & 0.4018 & & 0.6600 & 0.6600 & 0.5373 & \\ \cline{2-9}  & 0.5 & 8 & 0.5983 & 0.5600 & **0.6613** & 0.5528 & 0.5713 \\ \cline{2-9}  & 0.8 & 14 & 0.5720 & 0.5713 & 0.6378 & 0.5898 & \\ \hline \multirow{3}{*}{D2} & 0.2 & 18 & 0.3188 & 0.5370 & **0.5408** & 0.3117 & \\ \cline{2-9}  & 0.5 & 44 & 0.3145 & 0.3151 & 0.3264 & 0.3152 & 0.4045 \\ \cline{2-9}  & 0.8 & 70 & 0.3143 & 0.4045 & 0.3157 & 0.3153 & \\ \hline \multirow{3}{*}{D3} & 0.2 & 3 & 0.3201 & 0.3207 & **0.4512** & 0.3207 & \\ \cline{2-9}  & 0.5 & 7 & 0.3198 & 0.3426 & 0.3168 & 0.3746 & 0.3198 \\ \cline{2-9}  & 0.8 & 11 & 0.3198 & 0.3198 & 0.3148 & 0.3198 & \\ \hline \multirow{3}{*}{D4} & 0.2 & 3 & 0.5307 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-9}  & 0.5 & 7 & **0.5357** & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-9}  & 0.8 & 11 & 0.3376 & Mem Ex & Mem Ex & Mem Ex & \\ \hline \multirow{3}{*}{D5} & 0.2 & 2 & 0.4989 & 0.4930 & 0.5351 & 0.5220 & \\ \cline{2-9}  & 0.5 & 5 & 0.4989 & 0.5389 & 0.4959 & 0.4940 & 0.4964 \\ \cline{2-9}  & 0.8 & 8 & 0.5000 & **0.6307** & 0.4964 & 0.4964 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clustering accuracy where four feature selection (FS) methods were applied, showing results for three feature percentages versus all features. Highest FS-based result is in bold.