UPM Institutional Repository

Improved expectation maximization algorithm for Gaussian mixed model using the kernel method


Citation

Mohd Yusoff, Mohd Izhan and Mohamed, Ibrahim and Abu Bakar, Mohd Rizam (2013) Improved expectation maximization algorithm for Gaussian mixed model using the kernel method. Mathematical Problems in Engineering, 2013. art. no. 757240. pp. 1-9. ISSN 1024-123X

Abstract

Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.


Download File

[img]
Preview
PDF (Abstract)
Improved expectation maximization algorithm for Gaussian mixed model using the kernel method.pdf

Download (83kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Science
DOI Number: https://doi.org/10.1155/2013/757240
Publisher: Hindawi Publishing Corporation
Keywords: Kernel method; Gaussian mixed model; Expectation Maximization algorithm
Depositing User: Umikalthom Abdullah
Date Deposited: 14 Nov 2014 03:32
Last Modified: 08 Oct 2015 03:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1155/2013/757240
URI: http://psasir.upm.edu.my/id/eprint/30397
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item