# Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1
## 3 Results
### Experimental Results  
Different classifiers along with the proposed voting and boosting methods are experimented with, and Table 3 shows the results of all the classifiers.  
As seen in Table 3, the highest accuracy obtained for the prediction of transcription factor binding sites of SP1 on human chromosome is 92% using KNN and 88.1% using voting. On the other hand, the sensitivity is recorded highest for SVM and KNN, 92.1% and 90.3%, respectively. Specificity confirms the outperformance of KNN and voting, with 93.6% and 84.3%, respectively. Finally, the AUC is 97% for KNN and 95% for SVM and voting. The results show the high performance of KNN, SVM and voting. These results outperform the work found in the literature, which recorded the highest accuracy of 84.4%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **AUC\%** \\ \hline Logistic Regression & 83.7 & 84.2 & 83.2 & 93 \\ \hline LDA & 83.5 & 84.2 & 82.8 & 93 \\ \hline KNN & 92 & 90.3 & 93.6 & 97 \\ \hline Decision Trees & 75.2 & 73.3 & 76.8 & 73 \\ \hline Naive Bayes & 72.5 & 67.2 & 77.2 & 80 \\ \hline SVM & 86.2 & 92.1 & 80 & 95 \\ \hline AdaBoost & 83.5 & 84.2 & 82.8 & 93 \\ \hline GradientBoosting & 84.5 & 87.7 & 81.6 & 93 \\ \hline Random forest & 86.2 & 88.2 & 84 & 94 \\ \hline Extra Trees & 86.8 & 89.9 & 84 & 94 \\ \hline MLP & 83 & 86.8 & 81.2 & 93 \\ \hline Boosting (Logistic Regression+SVM) & 87 & 88.9 & 85.1 & 95 \\ \hline Proposed Voting (SVM+MLP) & 88.1 & 88.5 & 87.7 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Each Classifier.  
The voting classifier is implemented by ensembling two classifiers based on the results of the accuracy and AUC percentage. The ensembled classifiers are the SVM and MLP classifiers. The voting model was implemented with a soft voting algorithm. It was found that the best results of the voting classifier were achieved by giving equal weight to each classifier in the ensemble.  
Several voting techniques were attempted. SVM with Extra Trees achieved an accuracy of 87%, with sensitivity, specificity and AUC of 91.4%, 86.2% and 95%, respectively. SVM with Logistic Regression achieved an accuracy of 87.9%, with sensitivity, specificity and AUC of 88.5%, 87.3% and 95%, respectively. SVM with Logistic Regression and Extra Trees achieved an accuracy of 86.6%, with sensitivity, specificity and AUC of 88.1%, 85.1% and 95%, respectively. SVM with MLP outperformed similar models and achieved an accuracy of 88.1%, with sensitivity, specificity and AUC of 88.5%, 87.1% and 95%, respectively. These results show that the voting proposed enhances the accuracy performance of the sole classifier.  
Moreover, boosting was also proposed to enhance the classification accuracy, where the developed voting scheme was used as a base learner for gradient boosting. Random Forest and Naive Bayes achieved an accuracy score of 85.8%, sensitivity of 85.6%, specificity of 86% and AUC of 94%. Decision Trees with Naive Bayes obtained 82.5%, 83.1%, 81.7% and 92%, for accuracy, sensitivity, specificity and AUC, respectively. Extra Trees and Naive Bayes obtained 86%, 86.6%, 85.1% and 94%, for accuracy, sensitivity, specificity and AUC, respectively. Logistic Regression and Naive Bayes obtained 84.7%, 86.4%, 83% and 94%, for accuracy, sensitivity, specificity and AUC, respectively. SVM and Logistic Regression obtained 87%, 88.9%, 85.1% and 95%, for accuracy, sensitivity, specificity and AUC, respectively.  
Table 3 shows the best performing classifier in voting and in boosting. In the boosting classifier proposed, the base classifier used is obtained from voting between the proposed machine learning techniques. Figure 1 shows the precision recall curve of the proposed methods to show the tradeoff between precision and recall, where the KNN and proposed voting and boosting show an area of 0.97 and 0.95, consecutively. This confirms the high performance of KNN and the proposed voting scheme.  
Figure 1: _Cont._  
Figure 1: _Cont_.