The true class regions are spotlighted through the symbol color (black for infested class; red for uninfested class). Similarly, the predicted class regions are visualized using a colored background that has been successfully built from the optimization process, i.e., white background for infested class and grey background for uninfested class. Figure 7.Visualization the optimal hyperplane generated by polynomial kernel function. 4. Discussion It should be reiterated that the current study is based on training data sets to build the classification model generated by various kernel functions and to evaluate its performance. In this study, from the four types of proposed kernels, polynomial was selected as the most effective kernel to apply into our system. The rule of this kernel is to calculate the value of ?(??)in Equation (7); if the result gives a positive sign (“+”), then it is classified as termite infestation, and vice versa. This investigation is a challenging issue, because we must be able to separate the signals generated by termites and noise from the environment. Information about the presence of termites in the wood is crucial. Some of the benefits gained when this system was successfully built enable early detection, as well as help us maintain and prevent higher termite attacks on wood products. The investigation results prove scientifically that the termite acoustic signal generated by the activity Figure 7.Visualization the optimal hyperplane generated by polynomial kernel function.The SVM classifier can manage the large features spaces, avoid overfitting by controlling themargin, and also represent using some number of samples as informative points, well-known assupport vectors (SVs). The SVs give the solution to the problem in this study; if all training data setsare retrained, then this solution will not change. It can be ensured that all the characteristics in thetraining data set can be represented by the SVs. This is a crucial property when analyzing large datasets consisting of many uninformative patterns . In common case, the number of SVs is smallerthan the total training data set. In the Figure7, the total training data are shown by circle and cross;the black circle “o” and red circle “o” describe the training dataset for infested and uninfested classrespectively. However, not all the training data become SVs. For example, in this study, the numbers ofsupport vectors employed to take a decision are 60 SVs (Figure7), symbolized by the cross; the blackcross “x” is used for SVs at infested class and red cross “x” depicts SVs for uninfested class. The trueclass regions are spotlighted through the symbol color (black for infested class; red for uninfestedclass). Similarly, the predicted class regions are visualized using a colored background that has beensuccessfully built from the optimization process, i.e., white background for infested class and greybackground for uninfested class.