A self-adaptive very fast simulated annealing based on Hidden Markov model

2017 ◽  
Author(s):  
Mohamed Lalaoui ◽  
Abdellatif El Afia ◽  
Raddouane Chiheb
Keyword(s):  
Simulated Annealing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Very Fast Simulated Annealing ◽  
Self Adaptive

scholarly journals A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Liu ◽  
Dashi Luo ◽  
Ming Liu ◽  
Jun Zhong ◽  
Ye Wei ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Training Dataset ◽  
Support Vector ◽  
Emotion Classification ◽  
Online Social Media ◽  
Hidden Knowledge ◽  
Self Adaptive

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that theF-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

scholarly journals A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

2018 ◽  
Vol 8 (1) ◽  
pp. 291 ◽  
Author(s):  
Mohamed Lalaoui ◽  
Abdellatif El Afia ◽  
Raddouane Chiheb
Keyword(s):  
Simulated Annealing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Simulated Annealing Algorithm ◽  
Learning Algorithm ◽  
Hidden Markov ◽  
Main Idea ◽  
Self Tuning ◽  
Annealing Algorithm ◽  
Search History

Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

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