Classification of DNA sequences: Analysis of performance of SVM using a novel integral kernel function
Keywords:
DNA, K-mer, Machine learning, RBF, Polynomial, Integral KernelAbstract
The DNA sequence classification plays a vital role in bioinformatics to categorize the unknown DNA sequence. In the present article a new approach for DNA classification has been proposed. DNA sequences belong to six different classes are put under the light of present research for classification. A popular classification model SVM has been analysed for exploring the insights of it. Two kernel functions namely Radial Basis and Polynomial are kept under deep analysis for integrating these kernels. These two kernel functions are successfully integrated. An experiment has been carried out by integrating Radial Basis Function with Polynomial kernel. The performance has been measured using metrics such as Precision, Recall, F1-score, and Accuracy. The performance of the methods adopted in the present research has also been shown using Precision-Recall curve and ROC curve. The individual accuracy achieved by Radial Basis Function and Polynomial function is 80.6% and 82.7% respectively. The proposed novel model has achieved accuracy of 98.4%. The result clearly shows that the proposed integral kernel has outperformed the Radial Basis and Polynomial functions.
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