A New Algorithm for Hidden Markov Models Learning Problem
This work addresses the validation and performance of HMM learning algorithms, which is incremental as it builds on existing population-based methods.
The paper tackled the problem of learning Hidden Markov Models (HMMs) by comparing existing algorithms, providing a validation tool, and proposing a new algorithm called Modified Asexual Reproduction Optimization (MARO), which outperformed other methods in accuracy and robustness on nine benchmark datasets.
This research focuses on the algorithms and approaches for learning Hidden Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process. One of the essential characteristics of HMMs is their learning capabilities. Learning algorithms are introduced to overcome this inconvenience. One of the main problems of the newly proposed algorithms is their validation. This research aims by using the theoretical and experimental analysis to 1) compare HMMs learning algorithms proposed in the literature, 2) provide a validation tool for new HMM learning algorithms, and 3) present a new algorithm called Asexual Reproduction Optimization (ARO) with one of its extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the validation tool proposed. According to the literature findings, it seems that populationbased algorithms perform better among HMMs learning approaches than other algorithms. Also, the testing was done in nine benchmark datasets. The results show that MARO outperforms different algorithms in objective functions in terms of accuracy and robustness.