A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

Alhaj, Yousif A.; Dahou, Abdelghani; Al-qaness, Mohammed A. A.; Abualigah, Laith; Abbasi, Aaqif Afzaal; Almaweri, Nasser Ahmed Obad; Elaziz, Mohamed Abd; Damaševičius, Robertas

doi:10.3390/fi14070194

Cited by 21 publications

(5 citation statements)

References 62 publications

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“…Stemming in the English language may be very easy; however, other languages, such as Arabic, require custom stemming algorithms to [80]. To improve the results, heuristic optimization methods have been applied to improve feature selection [100]. Other methods, such as redundant feature mapping [101] and word co-occurrences [102], can also help with the classification process to improve the performance.…”

Section: Feature Selectionmentioning

confidence: 99%

Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review

Palanivinayagam

El-Bayeh²,

Damaševičius

2023

Algorithms

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Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gaps in the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification.

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Section: Feature Selectionmentioning

confidence: 99%

Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review

Palanivinayagam

El-Bayeh²,

Damaševičius

2023

Algorithms

Self Cite

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“…Authors in [21] formulated feature subsets with Chi-square, Gini index, and PSO algorithms to solve FS problems in machine learning. Authors in [22] proposed the algorithm FS two-stage to enhance the effectiveness of Arabic text classification by combining the term frequency-inverse document frequency in the first phase and particle swarm optimization in the second phase. The ant lion optimizer, which was utilized in a dataset for COVID-19, was introduced in [23] as a hybrid technique for addressing the feature selection difficulty.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

2023

IJIES

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This paper proposes an improved binary sparrow search algorithm (IBSSA) as a search strategy within the feature selection (FS) methods. Its main objective is to use clinical texts to improve COVID-19 patient categorization. The constant need for an efficient FS system and the favorable outcomes of swarming behavior in numerous optimization situations drove our efforts to develop a novel FS strategy. Additionally, clinical text data are frequently highly dimensional and contain uninformative features, which have a major impact on the classifier's accuracy, making FS a key machine-learning step in data pre-processing to reduce data dimensionality. The bi-stage FS approach is used in this work to elect the features. At the initial stage, we employed a term weighting scheme (TWS) that assigned a weighted score to each feature by measuring the significance of the features obtained from the pre-processing model using a new weight calculation method called root term frequency-core-inverse exponential frequency (RTF-C-IEF). Next, finding the most relevant and almost optimal feature subset for COVID-19 illness diagnosis is done in the second stage using a freshly developed methodology that was inspired by the way sparrow's behavior. The suggested modification method for the sparrow's algorithm is composed of several stages of advancement. The main objectives are to promote the exploration of the search space and increase the algorithm's variability. In order to evaluate the proposed model, various classifiers were employed on two datasets, each of which had 1446 and 3053 cases, respectively. The experimental and statistical results demonstrate that the proposed IBSSA is significantly superior compared to other comparative optimization algorithms, and it successfully upgrades the shortcomings of the original SSA. Moreover, the IBSSA has the highest accurate performance when compared to other rivals by the SVM classifier, Where, average removed features are 77.99% and 83.5%, with improvement percentages by F1-scores: 84.95% and 95.94 % for both datasets, respectively.

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“…Then, the preparation methods were utilized to score the files for classification. Alhaj et al [18] developed a new TC technique to improve the performance of the ATC process utilizing ML approaches. The identification of an appropriate Feature Selection (FS) methodology along with an ideal sum of the features remains the most important step in the ATC process to achieve the finest classification outcomes.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Duhayyim¹,

Al-onazi²,

Nour³

et al. 2023

Computer Systems Science and Engineering

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Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the 'Text Classification' process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

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A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

Cited by 21 publications

References 62 publications

Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review

Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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