Firefly Algorithm based Feature Selection for Arabic Text Classification

Marie-Sainte, Souad Larabi; Alalyani, Nada

doi:10.1016/j.jksuci.2018.06.004

Cited by 84 publications

(51 citation statements)

References 18 publications

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“…Additional research looked at algorithms for optimization such as Firefly and Cuckoo search. The Firefly algorithm in the paper [10] was used with SVM. The researchers experimented with the Arabic text with feature selection.…”

Section: B Bio-inspired Methodsmentioning

confidence: 99%

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

et al. 2020

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Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This paper aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.

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Section: B Bio-inspired Methodsmentioning

confidence: 99%

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

et al. 2020

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“…Related to text classification, the Arabic Text Classification system (ATC-FA) is proposed in [ 21 ]; this system combines the algorithm of Support Vector Machines (SVM) with an intelligent Feature Selection method (FS) based on the Firefly Algorithm (FA). Genetic programming has been used in [ 22 ] to generate alternative term-weighting schemes (TWSs) in text classification, allowing to improve the performance of current schemes in text classification by combining TWSs, terms (TRs), and term-document (TDRs) with a predefined set of operators.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Optimization of Ensemble Learning to Determine Sentiment Polarity in an Unbalanced Multiclass Corpus

García-Mendoza

Gambino

Villarreal-Cervantes

et al. 2020

Entropy

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Sentiment polarity classification in social media is a very important task, as it enables gathering trends on particular subjects given a set of opinions. Currently, a great advance has been made by using deep learning techniques, such as word embeddings, recurrent neural networks, and encoders, such as BERT. Unfortunately, these techniques require large amounts of data, which, in some cases, is not available. In order to model this situation, challenges, such as the Spanish TASS organized by the Spanish Society for Natural Language Processing (SEPLN), have been proposed, which pose particular difficulties: First, an unwieldy balance in the training and the test set, being this latter more than eight times the size of the training set. Another difficulty is the marked unbalance in the distribution of classes, which is also different between both sets. Finally, there are four different labels, which create the need to adapt current classifications methods for multiclass handling. Traditional machine learning methods, such as Naïve Bayes, Logistic Regression, and Support Vector Machines, achieve modest performance in these conditions, but used as an ensemble it is possible to attain competitive execution. Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose. The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques.

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“…They also compared this improved chi-square with three traditional features selection metrics namely mutual information, information gain and Chi-square. Another study in [17] introduced a new firefly algorithm based feature selection method which achieved a precision value equals to 0.994 on an Open Source Arabic Corpora (OSAC) dataset.…”

Section: Related Work On Text Classificationmentioning

confidence: 99%

Deep Learning Approach for Automatic Topic Classification in an Online Submission System

Điện¹,

Thanh-Hai²,

Nguyen³

2020

Adv. sci. technol. eng. syst. j.

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Topic classification is a crucial task where knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. An application of topic classification is article (e.g., journal/conference paper) classification which is very useful for online submission systems. In fact, numerous online journals/magazine submission systems usually receive thousands of article submissions or even more for each month. This leads to a huge amount of time-consumption of editors to process and categorize the submissions aiming to look for and assign appropriate reviewers to the submitted articles. In this study, we propose an approach based on natural language processing techniques and machine learning algorithms (both classic machine learning and deep learning) to automatic classify the topics of articles in an online submission system. We show by promising performance collected from prediction tasks to present that the proposed method is a potential approach for applying to the real system.

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Firefly Algorithm based Feature Selection for Arabic Text Classification

Cited by 84 publications

References 18 publications

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Evolutionary Optimization of Ensemble Learning to Determine Sentiment Polarity in an Unbalanced Multiclass Corpus

Deep Learning Approach for Automatic Topic Classification in an Online Submission System

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