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Activity Number: 614
Type: Contributed
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #313548
Title: Using Genetic Algorithms to Improve the Results of the EM Algorithm for Finite Mixture Models
Author(s): Sachith Abeysundara*+ and Byungtae Seo
Companies: and Sungkyunkwan University
Keywords: Finite Mixture Models ; EM Algorithm ; Genetic Algorithm
Abstract:

Finite mixture models have been receiving important attention over the years from a practical and theoretical point of view, but it is still a challenging task to estimate a reasonable estimator based on the maximum likelihood method. Researchers have done a lot of work to improve the results of the Expectation-Maximization algorithm by modifying its basic idea. This work presents such an attempt to obtain better estimates for a finite normal mixture model. A traditional evolutionary technique, known as the Genetic algorithm (GA), is coupled with the EM algorithm to improve the estimates of the EM algorithm starting with a random initial vector of parameters. Once the EM algorithm converged to a certain parameter vector with a specific likelihood value, the GA is used to optimize the difference of the complete log-likelihood function and the converged log-likelihood. The presented method is tested with the availability of a Non-penalized and Penalized likelihood functions. Based on the simulation results, we can see that the proposed method is always superior to the classical EM algorithm when concerning the global maximizer in the mixture likelihood function.


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