A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization
HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called “AntMarkov” to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was co...
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doaj-a7ffe21054fd45ae91e2b346ad9d08772020-11-25T02:49:21ZengElsevierHeliyon2405-84402019-03-0153e01299A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony OptimizationAkram Emdadi0Fatemeh Ahmadi Moughari1Fatemeh Yassaee Meybodi2Changiz Eslahchi3Department of Mathematics, Shahid-Beheshti University, Tehran, IranDepartment of Mathematics, Shahid-Beheshti University, Tehran, IranDepartment of Mathematics, Shahid-Beheshti University, Tehran, IranDepartment of Mathematics, Shahid-Beheshti University, Tehran, Iran; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Corresponding author.HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called “AntMarkov” to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was conducted on 5 different simulated datasets with different features. For further evaluation, we analyzed the performance of algorithms on the prediction of protein secondary structures problem. The results demonstrate that our algorithm obtains better results with respect to the results of the other algorithms in terms of time efficiency and the amount of similarity of estimated parameters to the original parameters and log-likelihood.The source code of our algorithm is available in https://github.com/emdadi/HMMPE.http://www.sciencedirect.com/science/article/pii/S2405844018341203Computer science |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Akram Emdadi Fatemeh Ahmadi Moughari Fatemeh Yassaee Meybodi Changiz Eslahchi |
spellingShingle |
Akram Emdadi Fatemeh Ahmadi Moughari Fatemeh Yassaee Meybodi Changiz Eslahchi A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization Heliyon Computer science |
author_facet |
Akram Emdadi Fatemeh Ahmadi Moughari Fatemeh Yassaee Meybodi Changiz Eslahchi |
author_sort |
Akram Emdadi |
title |
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization |
title_short |
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization |
title_full |
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization |
title_fullStr |
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization |
title_full_unstemmed |
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization |
title_sort |
novel algorithm for parameter estimation of hidden markov model inspired by ant colony optimization |
publisher |
Elsevier |
series |
Heliyon |
issn |
2405-8440 |
publishDate |
2019-03-01 |
description |
HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called “AntMarkov” to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was conducted on 5 different simulated datasets with different features. For further evaluation, we analyzed the performance of algorithms on the prediction of protein secondary structures problem. The results demonstrate that our algorithm obtains better results with respect to the results of the other algorithms in terms of time efficiency and the amount of similarity of estimated parameters to the original parameters and log-likelihood.The source code of our algorithm is available in https://github.com/emdadi/HMMPE. |
topic |
Computer science |
url |
http://www.sciencedirect.com/science/article/pii/S2405844018341203 |
work_keys_str_mv |
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1724744027158347776 |