Abstract
The effectiveness of a classifier is highly dependent on the discriminative power of the feature vectors extracted from the dataset. In this study a novel feature vector is presented that aims at better classification of short protein coding DNA. For this feature vector a straightforward ensemble method, Adaboost.M1 in conjunction with Multilayer Perceptron (MLP) as the base classifier was employed. The proposed model shows 97.36% accuracy, 97.76% sensitivity and 96.82% specificity. The results demonstrate that the proposed feature vector is promising, and help in increasing the prediction accuracy.
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Goli, B., B.L., A., Joy, C., Nair, A.S. (2012). New Feature Vector for Recognition of Short Microbial Genes. In: Mathew, J., Patra, P., Pradhan, D.K., Kuttyamma, A.J. (eds) Eco-friendly Computing and Communication Systems. ICECCS 2012. Communications in Computer and Information Science, vol 305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32112-2_27
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DOI: https://doi.org/10.1007/978-3-642-32112-2_27
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