Arabic Text Classification: A Review Study on Feature Selection Methods

The paper proposes a hybrid approach to reduce dimension in text classification problems, to overcome the issue of Curse of Dimensionality. This hybrid approach is a combination of Feature Selection (FS) and Feature Extraction (FE) methods, considering different aspects of feature relevance, to effectively reduce the dimension in large text datasets. It prevents feature selection biased in favor of a particular FS method. Many FS methods like Term Variance, Document Frequency, Information Gain, Shannons Entropy measure, Mean-Median and Mean Absolute Difference, were implemented and a comparative study was made on their performance when implemented in a hybrid system. The features selected by the individual FS methods are merged using three approaches, namely, Union, Intersection and Modified Union. The sub lists of features further undergo Feature Extraction by PCA, and the reduced feature sub list is clustered with k-means. Finally, the sentiment-score of the individual clusters are calculated using SentiWordNet database which gives the polarity of the data. The experiments were conducted on the benchmark datasets namely Reuters-21,578 and Classic4. The performance evaluation of the system made using the measures like precision, recall, f-score and accuracy shows that the proposed method has improved performance compared to its competitive methods.

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.

QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods

Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and feature extraction techniques remove the irrelevant features from the text documents and reduce the dimensionality of feature space. This paper presents the various feature selection and feature extraction methods. This paper also discusses various classifiers to classify the documents.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

Over the last years, Natural Language Processing (NLP) for Arabic language has obtained increasing importance due to the massive textual information available online in an unstructured text format, and its capability in facilitating and making information retrieval easier. One of the widely used NLP task is “Text Classification”. Its goal is to employ machine learning technics to automatically classify the text documents into one or more predefined categories. An important step in machine learning is to find suitable and large data for training and testing an algorithm. Moreover, Deep Learning (DL), the trending machine learning research, requires a lot of data and needs to be trained with several different and challenging datasets to perform to its best. Currently, there are few available corpora used in Arabic text categorization research. These corpora are small and some of them are unbalanced or contains redundant data. In this paper, a new voluminous Arabic corpus is proposed. This corpus is collected from 16 Arabic online news portals using an automated web crawling process. Two versions are available: the first is imbalanced and contains 3252934 articles distributed into 8 predefined categories. This version can be used to generate Arabic word embedding; the second is balanced and contains 720000 articles also distributed into 8 predefined categories with 90000 each. It can be used in Arabic text classification research. The corpus can be made available for research purpose upon request. Two experiments were conducted to show the impact of dataset size and the use of word2vec pre-trained word embedding on the performance of Arabic text classification using deep learning model.

Target spectrum based feature selection (TSFS): A new method based on chain coding for target detection problems

In the era of digital information, online platforms play a crucial role in shaping public opinion. However, the extensive spread of misinformation and fake news poses a significant challenge, largely fueled by non-credible users. Detecting user credibility is vital for ensuring the reliability of information on these platforms. This study employs supervised machine learning algorithms, leveraging key user features to enhance credibility detection. Feature selection methods, specifically SelectKBest and correlation-based algorithms, are explored for their impact on X-Platform user credibility detection. Utilizing various classifiers, including support vector machine, logistic regression, and XGBoost, experiments are conducted on the ArPFN dataset, which is a labeled, balanced, publicly available dataset. The evaluation includes measures like accuracy, precision, recall, and F1-score to assess efficiency. This research considers feature categories and selection methods with SML to detect their impact on the accuracy of X-Platform user credibility detection, making this research a reference for researchers and practitioners working in the field of SML, feature engineering, and social media analysis. We aim to advance the field’s understanding of effective strategies for mitigating the spread of fake news. The novelty of this study lies in the comprehensive exploration of feature selection methods and their influence on credibility detection, contributing valuable insights for future research in this domain.

Stock market prediction is a very noisy problem and the use of any additional information to increase accuracy is necessary. In this paper, for the stock daily return prediction problem, the set of features is expanded to include indicators not only for the stock to be predicted itself but also a set of other stocks and currencies. Afterwards, different feature selection and classification methods are utilized for prediction. The daily close returns of the 3 most traded stocks (GARAN, THYAO, and ISCTR) in Borsa Istanbul (BIST) are predicted using indicators computed on those stocks, indicators for all the other stocks listed in the BIST100 index, and indicators on the dollar-gold prices. Twenty-five different indicators on daily stock prices are computed to form feature vectors for each trading day. These feature vectors are assigned class labels according to the daily close returns. Expanding the feature space with BIST100 stocks features results in a high dimensional feature space, with possibly noisy or irrelevant features. Therefore, feature selection methods are utilized to select the most informative features. In order to determine relevance scores of features, fast filter-based methods, gain ratio and relief, are used. Experiments are performed based on individual stock features, dollar-gold features (DG), BIST100 stock features (BIST100), and a combination of BIST100 and DG with and without feature selection. Using the gain ratio feature selection with a gradient boosting machine (GBM), the movements of GARAN stock were predicted with an accuracy of 0.599 and an F-measure of 0.614. For THYAO, the relief feature selection with the GBM gave an accuracy of 0.558, and for ISCTR, the gain ratio feature selection with logistic regression achieved an accuracy of 0.581. It was found that using BIST100 stock features boosts classification results for all stocks in terms of accuracy.

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

With the development of technology, large databases become more accessible thanks to automation systems that automatically keep data and allow the use of large databases in many areas. Machine learning approaches, a sub-branch of artificial intelligence, are used in making decisions about the process by analyzing the data stored in databases and converting them into information. In this paper, the body production process of the surgical (medical) mask is analyzed. As it is known, surgical masks have become a part of our lives by becoming widespread all over the world with the COVID-19 pandemic. In the surgical mask body production process, using the real data of the production factors, first of all, filtering feature selection methods and analyzes were made and the feature selection method to be used was determined. With the specified feature selection method, the factors affecting the product quality are determined. Secondly, machine learning methods were used to determine the values and value ranges of factors (features) in the production of defect-free products. The performances of the machine learning models established in the second stage were increased by feature selection analysis. In the study, together with the parameter optimizations made to machine learning algorithms, it was seen that the best algorithm to estimate the defective product rate was the Ibk algorithm with 92.3% accuracy, 91.9% F measurement and 93% AUC value. Finally, in line with the decision rules revealed in the study, it was observed that the fabric types used for the upper/middle/lower layers that make up the body part in the mask body production process greatly affect the rates of defective or defect-free products. If the rod apparatus around the nose belongs to class k, it has been determined that many masks are defective. Improvement suggestions were presented according to the application results.

• Neural Network feature selection methodology for both numerical and categorical data. • Embedded feature selection provides model and feature subset in a single training. • Novel feature embedding for dealing with high-cardinality categorical features. • Validation of the methodology on several open and real industry dataset. • Comparison with several state-of-the-art feature selection methodologie. In an era of effortless data collection, the impact of machine learning — especially neural networks (NNs) — is undeniable. As datasets grow in size and complexity, efficiently handling mixed data types, including categorical and numerical features, becomes critical. Feature encoding and selection play a key role in improving NN performance, efficiency, interpretability, and generalisation. This paper presents GLEm-Net (Grouped Lasso with Embeddings Network), a novel NN-based approach that seamlessly integrates feature encoding and selection directly into the training process. GLEm-Net uses embedding layers to process categorical features with high cardinality, simplifying the model and improving generalisation. By extending the grouped Lasso regularisation to explicitly consider categorical features, GLEm-Net automatically identifies the most relevant features during training and returns them to the analyst. We evaluate GLEm-Net on open and proprietary industry datasets and compare it to state-of-the-art feature selection methodologies. Results show that GLEm-Net adapts to each dataset by allowing the NN to directly select subsets of most important features, offering on par performance with the best state-of-the-art feature selection methods, while eliminating the need for the external feature encoding and selection steps that are now incorporated in the NN training stage.

In recent years, the integration of machine learning (ML) techniques into sales forecasting has garnered significant attention, revolutionizing traditional approaches and enhancing predictive accuracy. Among the myriad factors influencing the success of ML models, the selection of relevant features stands out as a critical determinant. This review paper systematically examines the impact of various feature selection methods on the performance of machine learning models specifically applied to sales forecasting. The exploration begins by delineating the fundamental importance of accurate sales forecasting in modern business contexts, emphasizing its role in strategic decision-making and resource allocation. Subsequently, an in-depth analysis of the diverse feature selection methods commonly employed in the literature is presented. Techniques ranging from filter methods, wrapper methods, to embedded methods are scrutinized for their ability to enhance model efficiency by identifying and incorporating the most informative features. A substantial portion of this review is devoted to elucidating the interplay between feature selection and the choice of machine learning algorithms. The examination encompasses popular algorithms such as linear regression, decision trees, support vector machines, and ensemble methods. The nuanced relationship between specific feature selection methods and the efficacy of each algorithm in the context of sales forecasting is thoroughly investigated. Moreover, the review extends its purview to encompass real-world applications and case studies where the impact of feature selection on sales forecasting models is empirically validated. By synthesizing findings from diverse studies, the paper aims to distill overarching trends and patterns, shedding light on the generalizability of feature selection methods across different industry domains and dataset characteristics.

Today, video data, as a powerful multimedia component, is accompanied by some problems with increasing usage in communication, health, education, and social media in particular. Classification and detection of concepts in video data by automatic methods are some of these challenging problems. In this study, we propose a video classification system, which incorporates deep convolutional neural networks (CNNs) by leveraging feature selection and data fusion techniques to improve the accuracy of the classification. Principal Component Analysis (PCA) as a feature selection method and Discriminant Correlation Analysis (DCA) technique, which incorporates class associations into the correlation analysis of feature sets for data fusion, are applied to the problem at the feature level. Support Vector Machines (SVMs) have been trained with new feature vectors obtained from different deep convolutional neural networks by feature selection and data fusion methods. The proposed method is tested for 38 concepts on TRECVID 2013 SIN video task dataset and the results are evaluated. Our results show that the classification accuracy is improved by 4% with an accuracy of 50.27% when the proposed data fusion and feature selection techniques are used.