# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 5 Experimental Evaluation
### Accuracy Evaluation  
In this section, we evaluate the accuracy of the query running time prediction algorithms. We train the respective neural network with 60% of the queries and we use 40% of the remaining queries for the experiments. We evaluate accuracy with \(1,5,10,25\) and 50 neurons in the hidden layer where we use the log-Sigmoid transfer function in the hidden layer and the linear transfer function in the output layer. Table 4 shows the accuracy achieved by the query running time prediction algorithms. Numbers in bold font denote the best accuracy results in each case. Our DFT-based \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**CueWeb09**} & \multicolumn{6}{c}{**Gov2**} \\ \hline  & \multicolumn{3}{c}{**BM25**} & \multicolumn{3}{c}{**TF-IDF**} & \multicolumn{3}{c}{**BM25**} & \multicolumn{3}{c}{**TF-IDF**} \\ \hline  & **RMSE** & **VC** & **PrC** & **RMSE** & **VC** & **PrC** & **RMSE** & **VC** & **PrC** & **RMSE** & **VC** & **PrC** \\ \hline \multicolumn{10}{c}{\(10\)-folds} \\ \hline Linear & 0.15 & 1.1\% & 0.88 & 0.10 & 1.7\% & 0.94 & 0.12 & 2.2\% & 0.88 & 0.14 & 0.8\% & 0.96 \\ \hline MV & 0.05 & 4.0\% & 0.88 & 0.02 & 6.8\% & 0.94 & 0.04 & 4.3\% & 0.88 & 0.04 & 2.7\% & 0.96 \\ \hline EML-5 & 0.06 & 12.5\% & 0.83 & 0.03 & 12.2\% & 0.93 & 0.05 & 8.3\% & 0.83 & 0.03 & 14.3\% & 0.94 \\ \hline BP-5 & 0.04 & 5.5\% & 0.93 & 0.01 & 11.4\% & 0.98 & 0.03 & 5.4\% & 0.94 & 0.02 & 8.5\% & 0.98 \\ \hline RF & 0.04 & 5.2\% & 0.93 & 0.01 & 15.2\% & 0.98 & 0.03 & 9.7\% & 0.95 & 0.02 & 8.2\% & 0.98 \\ \hline SVM & 0.15 & 0.9\% & 0.92 & 0.10 & 2.0\% & 0.96 & 0.12 & 1.6\% & 0.93 & 0.14 & 1.2\% & 0.95 \\ \hline \multicolumn{10}{c}{\(5\)-folds} \\ \hline Linear & 0.15 & 0.3\% & 0.88 & 0.10 & 0.9\% & 0.94 & 0.12 & 1.9\% & 0.88 & 0.14 & 1.0\% & 0.96 \\ \hline MV & 0.05 & 2.04\% & 0.88 & 0.02 & 4.0\% & 0.94 & 0.04 & 2.2\% & 0.88 & 0.04 & 2.8\% & 0.96 \\ \hline EML-5 & 0.07 & 18.9\% & 0.79 & 0.03 & 9.4\% & 0.92 & 0.05 & 10.0\% & 0.8 & 0.04 & 17.4\% & 0.93 \\ \hline BP-5 & 0.04 & 4.0\% & 0.92 & 0.01 & 7.1\% & 0.98 & 0.03 & 6.2\% & 0.93 & 0.02 & 3.2\% & 0.98 \\ \hline RF & 0.04 & 3.8\% & 0.93 & 0.02 & 11.7\% & 0.98 & 0.03 & 5.5\% & 0.95 & 0.02 & 6.3\% & 0.98 \\ \hline SVM & 0.15 & 0.7\% & 0.92 & 0.10 & 1.0\% & 0.95 & 0.12 & 0.9\% & 0.93 & 0.14 & 0.5\% & 0.95 \\ \hline \multicolumn{10}{c}{\(3\)-folds} \\ \hline Linear & 0.15 & 1.1\% & 0.88 & 0.10 & 0.7\% & 0.94 & 0.12 & 0.9\% & 0.88 & 0.14 & 0.3\% & 0.96 \\ \hline MV & 0.05 & 0.9\% & 0.88 & 0.02 & 2.3\% & 0.94 & 0.04 & 1.2\% & 0.88 & 0.04 & 2.0\% & 0.96 \\ \hline EML-5 & 0.06 & 4.0\% & 0.85 & 0.03 & 4.3\% & 0.93 & 0.04 & 7.4\% & 0.87 & 0.03 & 10.6\% & 0.94 \\ \hline BP-5 & 0.04 & 1.9\% & 0.92 & 0.01 & 1.1\% & 0.98 & 0.03 & 3.3\% & 0.93 & 0.02 & 4.4\% & 0.98 \\ \hline RF & 0.04 & 2.1\% & 0.93 & 0.02 & 8.7\% & 0.98 & 0.03 & 5.8\% & 0.94 & 0.02 & 3.9\% & 0.98 \\ \hline SVM & 0.15 & 0.4\% & 0.92 & 0.10 & 0.6\% & 0.95 & 0.12 & 0.8\% & 0.92 & 0.14 & 0.5\% & 0.95 \\ \hline \multicolumn{10}{c}{\(2\)-folds} \\ \hline Linear & 0.15 & 0.2\% & 0.88 & 0.10 & 0.3\% & 0.94 & 0.12 & 0.3\% & 0.88 & 0.14 & 0.3\% & 0.96 \\ \hline MV & 0.05 & 0.6\% & 0.88 & 0.02 & 0.9\% & 0.94 & 0.04 & 3.1\% & 0.88 & 0.04 & 0.4\% & 0.96 \\ \hline EML-5 & 0.06 & 5.0\% & 0.84 & 0.02 & 4.7\% & 0.94 & 0.05 & 6.5\% & 0.84 & 0.03 & 0.8\% & 0.95 \\ \hline BP-5 & 0.04 & 2.1\% & 0.92 & 0.02 & 4.1\% & 0.98 & 0.03 & 4.6\% & 0.93 & 0.02 & 2.3\% & 0.98 \\ \hline RF & 0.04 & 2.0\% & 0.93 & 0.02 & 7.3\% & 0.97 & 0.03 & 5.3\% & 0.94 & 0.02 & 5.8\% & 0.98 \\ \hline SVM & 0.15 & 0.2\% & 0.92 & 0.10 & 0.2\% & 0.95 & 0.12 & 0.8\% & 0.92 & 0.14 & 1.1\% & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy obtained by the proposed DFT-based query running time prediction algorithm using different machine learning methods. algorithm achieves the lowest RSME values and the highest Pearson correlation (PrC) values in most cases. The baseline approach outperforms our proposal only for the BM-WAND with TF-IDF and using the Gov2 Web collection.