Grey wolf optimization based parameter selection for support vector machines

2016 ◽  
Vol 35 (5) ◽  
pp. 1513-1523 ◽  
Author(s):  
Sathish Eswaramoorthy ◽  
N. Sivakumaran ◽  
Sankaranarayanan Sekaran
Keyword(s):  
Design Methodology ◽  
Support Vector ◽  
Svm Classifier ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Vector Machines ◽  
Human Perceptions ◽  
Selection For

Purpose The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO). Design/methodology/approach The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters. Findings The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis. Originality/value A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram.

Categorization of fabric design using multi-class least-square support vector machine

2014 ◽  
Vol 26 (1) ◽  
pp. 58-66 ◽  
Author(s):  
A. Ghosh ◽  
T. Guha ◽  
R. Bhar
Keyword(s):  
Pattern Recognition ◽  
Design Methodology ◽  
Inequality Constraints ◽  
Recognition System ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Reasonable Degree ◽  
Vector Machines ◽  
Fabric Design

Purpose – The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM). Design/methodology/approach – In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features. Findings – The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM. Originality/value – The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.

A set of parameters for automatically annotating a Sentiment Arabic Corpus

2019 ◽  
Vol 15 (5) ◽  
pp. 594-615
Author(s):  
Guellil Imane ◽  
Darwish Kareem ◽  
Azouaou Faical
Keyword(s):  
Design Methodology ◽  
Support Vector ◽  
Training Set ◽  
Training Corpus ◽  
Content Type ◽  
Sentiment Lexicon ◽  
Vector Machines ◽  
Automated Processing ◽  
The Right ◽  
Large Corpus

Purpose This paper aims to propose an approach to automatically annotate a large corpus in Arabic dialect. This corpus is used in order to analyse sentiments of Arabic users on social medias. It focuses on the Algerian dialect, which is a sub-dialect of Maghrebi Arabic. Although Algerian is spoken by roughly 40 million speakers, few studies address the automated processing in general and the sentiment analysis in specific for Algerian. Design/methodology/approach The approach is based on the construction and use of a sentiment lexicon to automatically annotate a large corpus of Algerian text that is extracted from Facebook. Using this approach allow to significantly increase the size of the training corpus without calling the manual annotation. The annotated corpus is then vectorized using document embedding (doc2vec), which is an extension of word embeddings (word2vec). For sentiments classification, the authors used different classifiers such as support vector machines (SVM), Naive Bayes (NB) and logistic regression (LR). Findings The results suggest that NB and SVM classifiers generally led to the best results and MLP generally had the worst results. Further, the threshold that the authors use in selecting messages for the training set had a noticeable impact on recall and precision, with a threshold of 0.6 producing the best results. Using PV-DBOW led to slightly higher results than using PV-DM. Combining PV-DBOW and PV-DM representations led to slightly lower results than using PV-DBOW alone. The best results were obtained by the NB classifier with F1 up to 86.9 per cent. Originality/value The principal originality of this paper is to determine the right parameters for automatically annotating an Algerian dialect corpus. This annotation is based on a sentiment lexicon that was also constructed automatically.

scholarly journals Automatic parameterization of Support Vector Machines for Short Texts Polarity Detection

2017 ◽  
Author(s):  
Aurelio Sanabria Rodríguez ◽  
Edgar Casasola Murillo
Keyword(s):  
Support Vector Machines ◽  
Parameter Optimization ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grid Search ◽  
Classification Models ◽  
Grey Wolf ◽  
Redes Sociales ◽  
Vector Machines ◽  
Toma De Decisiones

Abstract: The information from social media is emerging as a valuable source in decision-making, unfortunately the tools to turn these data into useful information still need some work. Using Support Vector Machines for polarity detection in short texts are popular among researchers for their good results, but parameter optimization to train classification models is a complex and costly process. This article compares two algorithms for automated parameter optimization in the process of creating classification models for polarity detection: the recently created Grey Wolf Optimizer and the Grid Search, using accuracy and f-score metrics.  Spanish Abstract: Los datos provenientes de las redes sociales están emergiendo como una fuente valiosa de información para los procesos de toma de decisiones, desafortunadamente las herramientas para convertir estos datos en información útil todavía tienen mucho camino por recorrer. Utilizar máquinas de soporte vectorial para la detección de polaridad en textos cortos goza de popularidad entre los investigadores debido a sus buenos resultados. Sin embargo, la optimización de los parámetros necesarios para entrenar modelos es un proceso complejo y costoso. Este artículo compara dos algoritmos para la optimización automatizada de parámetros en el proceso de crear modelos de clasificación para la detección de polaridad: Optimizador de lobo gris y las búsqueda en malla, utilizando las métricas de precisión y valor-f.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

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