## 4 Discussion
This data is in line with the hypothesis that reducing low-impact features leads to simpler computation with an insignificant decline in the web phishing dataset recognition rate. As we can see in Tables 15 and 16, the accuracy is decrease as the number of features reduced, however the gap is small. For the first scenario--PPS + RFE, for example--the gap between full feature and the 10 features is less than 1% for reducing 78 features as shown on Table 16 on the second column. All experiments scenarios with two datasets shown no more than 2% gap of accuracy between full and 10 features set. The experimental result shows that reducing the features for binary classification (normal/phihing) class do not affect too much for the recognition ability of the model. Figures 5 and 6 also show that reducing the number of feature do not suffer to much on recognition rate. To decide the class, machine learning algorithms do not need to calculate all the impact of each feature in input space. Not all features share the similar impact to the class label, low correlation between input variable and the class label indicate that the data less powerful to decide the class of the input. Removing low correlation input will help to speed up the classification process. This effect clearly presented in the execution time in Tables 15 and 16 where smaller number of features need smaller amount of time. According to Tables 15 and 16, removing the 10 weakest features leads to a better recognition rate because involving features with low or negative correlation cause the model to learn from disrupting information. The proof that in both datasets, accuracy improved from 96.76 to 96.93 and 98.1 to 98.16 at the first removal of the 10 weakest features in dataset1 and dataset2, respectively. In the first removal of 10 features, correlation between a pair of features is evaluated. A pair of features with correlation indicates redundant information in input side. Removing one of a pair of correlated features will not suffer the recognition rate. Some of removed features with negative correlation to the target labels as shown in Tables 13 and 14 indicate that the features do not support the output class and therefore removing those features gives positive effect to the accuracy. Three studies on this topic utilize the same dataset, namely the Tan dataset [15]. The results of comparing the accuracy values with the number of features extracted for each study's methodology are presented in Table 17. According to the Table 17, our method outperformed the accuracy value from another method that uses the same dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features Subset**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Execution Time**} \\  & & **PPS + RFE** & **MICe + RFE** & **Spearman + RFE** & **PPS + RFE** & **MICe + RFE** & **Spearman + RFE** \\ \hline
87 (full) & 96.76\% & 96.76\% & 96.76\% & 0.969786 & 0.969786 & 0.969786 \\ \hline
77 (correlation only) & 96.93\% & 96.58\% & 96.32\% & 0.973809 & 0.968564 & 0.967281 \\ \hline
60 (correlation + RFE) & 96.79\% & 96.61\% & 96.73\% & 0.973166 & 0.970815 & 0.967000 \\ \hline
50 (correlation + RFE) & 96.61\% & 96.52\% & 96.35\% & 0.970255 & 0.967971 & 0.963443 \\ \hline
40 (correlation + RFE) & 96.61\% & 96.58\% & 96.38\% & 0.971377 & 0.968564 & 0.962919 \\ \hline
30 (correlation + RFE) & 96.47\% & 96.23\% & 96.38\% & 0.965171 & 0.963905 & 0.961832 \\ \hline
20 (correlation + RFE) & 95.97\% & 95.88\% & 95.82\% & 0.960969 & 0.959810 & 0.958136 \\ \hline
10 (correlation + RFE) & 95.88\% & 95.21\% & 95.04\% & 0.957109 & 0.950617 & 0.948853 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Feature subset accuracy using Spearman on Dataset 2. Figure 5: Comparison of all feature subset scenarios for dataset 1.