Accuracy test in identifying the splice site type of DNA sequences by using probabilistic neural networks and support vector machines

Djati Kerami

Abstract


It has been known that Probabilistic Neural Networks as machine learning is very fast in it’s computation time and give a better accuracy comparing to another type of neural networks, on solving a real-world application problem. In the recent years, Support Vector Machines has become a popular model over other machine learning. It can be analyzed theoretically and can achieve a good performance at same time. This paper will describe the use of those machines learning to solve pattern recognition problems with a preliminary case study in detecting the type of splice site on the DNA sequences, particularity on the accuracy level. The results obtained show that Support Vector Machines have a good accuracy level about 95 % comparing to Probabilistic Neural Networks with 92 % approximately.

Keywords


Probabilistic Neural Networks; Support Vector Machines; Splice sites type detection; Accuracy level;

Full Text:

PDF

References


D.F. Specht, Neural Networks, 3 (1991) 108.

L. Faucett, Fundamentals of Neural Network, Prentice Hall Inc, Englewood Cliffs, 1994.

V.Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.

M.A. Hearst. B. Schölkopf. S. Dumais. E. Osuna and J. Platt, IEEE Intelligent System, 1 (1998) 18.

V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, 1998.

C.J.C. Burges, Data Mining and Knowledge Discovery, 2 (1998) 955.

N.Cristianini, J.Shawe-Taylor, An Introduction to Support Vector Machines and other Kernel-Based

Learning Methods, Cambridge Univ.Press, 2000.

J.Shawe-Taylor, N.Cristianini, Kernel Methods for Pattern Analysis, Cambridge Univ.Press, 2004.

B. Bolat, T. Yildirim, Electrical & Electronic Engineering, Istanbul Univ., 4 (2004) 1137.

Molecular Biology Data Bases, http://www.ics.edu/~mlearn/ Mlsummary.html




DOI: https://doi.org/10.11113/mjfas.v1n1.10

Refbacks

  • There are currently no refbacks.


Copyright (c) 2014 Djati Kerami

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Copyright © 2005-2019 Penerbit UTM Press, Universiti Teknologi Malaysia. Disclaimer: This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this website.