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Comparative Analysis of feature selection algorithms for Computational Personality Prediction From Social Media

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2020年第3期7卷 587-599页

作者： Al Marouf, Ahmed Hasan, Md. Kamrul Mahmud, Hasan Daffodil Int Univ DIU Dept Comp Sci & Engn Dhaka 1207 Bangladesh Islamic Univ Technol IUT Dept Comp Sci & Engn Gazipur 1704 Bangladesh

With the rapid growth of social media, users are getting involved in virtual socialism, generating a huge volume of textual and image contents. Considering the contents such as status updates/tweets and shared posts/retweets, liking other posts is reflecting the online behavior of the users. Predicting personality of a user from these digital footprints has become a computationally challenging problem. In a profile-based approach, utilizing the user-generated textual contents could be useful to reflect the personality in social media. Using huge number of features of different categories, such as traditional linguistic features (character-level, word-level, structural, and so on), psycholinguistic features (emotional affects, perceptions, social relationships, and so on) or social network features (network size, betweenness, and so on) could be useful to predict personality traits from social media. According to a widely popular personality model, namely, big-five-factor model (BFFM), the five factors are openness-to-experience, conscientiousness, extraversion, agreeableness, and neuroticism. Predicting personality is redefined as predicting each of these traits separately from the extracted features. Traditionally, it takes huge number of features to get better accuracy on any prediction task although applying feature selection algorithms may improve the performance of the model. In this article, we have compared the performance of five feature selection algorithms, namely the Pearson correlation coefficient (PCC), correlation-based feature subset (CFS), information gain (IG), symmetric uncertainly (SU) evaluator, and chi-squared (CHI) method. The performance is evaluated using the classic metrics, namely, precision, recall, f-measure, and accuracy as evaluation matrices.

关键词： feature extraction Prediction algorithms Linguistics Facebook Tools Predictive models Chi-squared (CHI) method computational personality prediction feature selection algorithms information gain (IG) Pearson correlation coefficient (PCC) social media

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Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study

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JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS 2016年第1期17卷 221-233页

作者： Nankali, Saber Torshabi, Ahmad Esmaili Miandoab, Payam Samadi Baghizadeh, Amin Grad Univ Adv Technol Dept Elect & Comp Engn Kerman *** Iran Grad Univ Adv Technol Dept Biotechnol Inst Sci & High Technol & Environm Sci Kerman *** Iran

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based feature selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in comb

关键词： optimum location external markers feature selection algorithms tumor tracking external-beam radiotherapy

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Predicting Student Performance Using feature selection algorithms for Deep Learning Models 6

Predicting Student Performance Using Feature Selection Algor...

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16th Latin American Conference on Learning Technologies (LACLO)

作者： Padilha, Thereza P. P. Lumacad, Gemel Catrambone, Richard Univ Fed Paraiba Dept Exact Sci Rio Tinto Brazil St Ritas Coll Balingasag STEM Strand Dept Cagayan De Oro Philippines Georgia Inst Technol Sch Physychol Atlanta GA 30332 USA

ISBN: (纸本)9781665423588

feature selection is an integral process for feature engineering prior to deep learning (DL) model development. The idea is to reduce complexities of high - dimensional data structures by keeping only relevant information in the data mining process. The critical part in developing a DL model to predict student performance is the high - dimensionality of students' profiles which results in a DL model with low performance metrics. Students' profile/data involves different aspects such as demographic information, academic records, technological resources, social attitudes, family background and/or socio - economic status. Empirically, the diversity of these data produce complexity in terms of dimension. In this paper, we compared the effectiveness of four feature selection algorithms (Information Gain Based, ReliefF, Boruta and Recursive feature Elimination) on deep learning models using an educational dataset from Portugal. The effectiveness is measured using the following model performance metrics: training accuracy, validation accuracy, testing accuracy, kappa statistic, and f - measure. Results revealed the robustness of the Boruta algorithm in dimensionality reduction as it allowed the deep learning model to achieve its highest performance metrics compared to the utilization of other feature selection algorithms.

关键词： deep learning feature selection algorithms dimensionality reduction prediction

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Impact Evaluation of feature selection algorithms on Machine Learning-based Intrusion Detection 11

Impact Evaluation of Feature Selection Algorithms on Machine...

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11th International Conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Regragui, Youssef Mazighi, Abdellah Ballihi, Lahoucine Orhanou, Ghizlane Mohammed V Univ Fac Sci LRIT Rabat Morocco

ISBN: (纸本)9798350377873;9798350377866

With the rise of different types of cyber threats, an efficient intrusion detection system (IDS) becomes very crucial for the network security. In this paper, we aim to enhance the performance of the intrusion detection by involving different feature selection (FS) algorithms that identify relevant features from high-dimensional datasets, reduce complexity, and improve the model accuracy. We aspire also to prove the importance of the use of large and balanced datasets in enhancing the intrusion detection performances. To do that, we use different recent datasets taken individually or combined with each other. After the preprocessing of the datasets, we apply diverse FS algorithms, and train the machine learning models. The performance evaluation is performed using metrics like accuracy, precision, recall, and F1-score, with an emphasis on analyzing computational efficiency. The obtained results were conclusive and prove the importance of either a balanced dataset or the use of well chosen FS algorithms in improving the intrusion detection.

关键词： Intrusion Detection Machine Learning feature selection algorithms Network Security Data Augmentation Intrusion Detection System

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Using feature selection algorithms in Educational Data Three Case Studies 12

Using Feature Selection Algorithms in Educational Data Three...

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12th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Marques, M. Nicoletti, M. C. Guimaraes, M. P. FACCAMP PMCC Fac Campo Limpo Paulista Campo Limpo Paulista SP Brazil Univ Fed Sao Paulo UNIFESP UAB Sao Paulo SP Brazil

ISBN: (纸本)9781509050475

This paper discusses three case studies involving the use of feature subset selection algorithms, as well as feature ranking, based on real data on student performances in a course of a Bachelor of Computer Science program. The case studies aimed at investigating, as a step prior to the use of data mining algorithms for performance prediction, the effectiveness of feature selection methods, for reducing data volume.

关键词： feature selection algorithms data minining component educational data

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A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using feature selection and Classification algorithms

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JOURNAL OF MEDICAL SYSTEMS 2014年第3期38卷 18-18页

作者： Sen, Baha Peker, Musa Cavusoglu, Abdullah Celebi, Fatih V. Yildirim Beyazit Univ Dept Comp Engn Ankara Turkey Karabuk Univ Dept Comp Engn TR-78050 Karabuk Turkey

Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR);fast correlation based feature selection (FCBF);ReliefF;t-test;and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF);feed-forward neural network (FFNN);decision tree (DT);support vector machine (SVM);and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

关键词： EEG signals Classification algorithms feature selection algorithms Classification of sleep stage

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Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 80卷 323-339页

作者： Liu, Yang Bi, Jian-Wu Fan, Zhi-Ping Northeastern Univ Sch Business Adm Dept Management Sci & Engn Shenyang 110167 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Multi-class sentiment classification has extensive application backgrounds, whereas studies on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment classification is proposed, which includes two parts: 1) selecting important features of texts using the feature selection algorithm, and 2) training multi-class sentiment classifier using the machine learning algorithm. Then, experiments are conducted for comparing the performances of four popular feature selection algorithms (document frequency, CHI statistics, information gain and gain ratio) and five popular machine learning algorithms (decision tree, naive Bayes, support vector machine, radial basis function neural network and K-nearest neighbor) in multi-class sentiment classification. The experiments are conducted on three public datasets which include twelve data subsets, and 10-fold cross validation is used to obtain the classification accuracy concerning each combination of feature selection algorithm, machine learning algorithm, feature set size and data subset. Based on the obtained 3600 classification accuracies (4 feature selection algorithms x 5 machine learning algorithms x 15 feature set sizes x 12 data subsets), the average classification accuracy of each algorithm is calculated, and the Wilcoxon test is used to verify the existence of significant difference between different algorithms in multi-class sentiment classification. The results show that, in terms of classification accuracy, gain ratio performs best among the four feature selection algorithms and support vector machine performs best among the five machine learning algorithms. In terms of execution time, the similar comparisons are also conducted. The obtained results would be valuable for further improving the existing multi-class sentiment classifiers and developing new multi-class sentiment classifiers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-class sentiment classification Experimental comparison feature selection algorithms Machine learning algorithms

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Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第4期58卷 1-68页

作者： Bohmrah, Maneet Kaur Kaur, Harjot Guru Nanak Dev Univ Dept Comp Sci & Engn Amritsar 143001 Punjab India

Due to the high classification accuracy and fast computational speed offered by Deep Neural Networks (DNNs), they have been widely used for the design and development of automated Artificial Intelligence (AI) tools for the detection of various diseases. These tools, which are intensive computational learning models, hold tremendous significance in healthcare for identifying various diseases. The primary goal of this review is to understand the applicability and methodology for implementing DNNs, including computational costs, for the classification of distinct diseases from disparate medical imaging datasets. This study presents an extensive survey of DNNs along with their various hybridization forms. To achieve this, the research papers surveyed have been grouped into five categories: pretrained DNNs, hyperparameter-tuned optimized DNNs, hybrid DNNs and ML classifiers, hybrid models with optimization techniques, and meta-heuristics based feature selection DNNs. The major part of this review highlights the significant role of nature-inspired meta-heuristic techniques used for hyperparameter optimization or feature selection algorithms of DNNs. Besides the frameworks and computational costs, descriptions of disparate medical image datasets and image preprocessing techniques have also been discussed under each category. Furthermore, a comparative analysis for each category has been performed on the basis of different parameters, including the type and size of datasets used, image preprocessing, methodology (as per the mentioned category), and performance (in terms of classification accuracy). This study also presents a bibliometric analysis based on the publication count of various articles related to hyperparameter-tuned optimized DNNs and meta-heuristic based feature selection DNNs. This review aims to assist potential AI researchers in choosing the most sound and appropriate DNN-based techniques for disease detection and prediction, all consolidated into a one sing

关键词： Machine learning classifiers Deep neural Networks Meta-heuristic techniques Optimization feature selection algorithms Explainable AI

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Discriminative estimated cumulants-based automatic modulation classification over multipath fading channels

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TELECOMMUNICATION SYSTEMS 2025年第2期88卷 1-23页

作者： Kadoun, Iyad Bizaki, Hossein Khaleghi Malek Ashtar Univ Technol Dept Elect & Comp Engn Tehran Iran

Automatic modulation classification (AMC) is an essential task in intelligent receivers. AMC over multipath fading channels have two problems: The first problem is that the Higher-order moment (HOM)-based normalized channel coefficients estimator is not valid for some types of digital modulations. The second problem is about poor classification accuracy. This study addresses the aforementioned challenges through a multi-pronged approach. Firstly, it introduces a novel HOM-based normalized channel coefficient estimator applicable to a broad spectrum of digital modulation schemes. Secondly, it derives mathematical expressions for the estimated normalized HOMs and Higher-order Cumulants (HOCs) of the transmitted signal. Finally, the research employs feature selection algorithms to identify the most discriminatory estimated HOCs for Adaptive Modulation and Coding (AMC). Simulation results demonstrate that the classification accuracy using estimated Higher-order Cumulants (HOCs) for M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) schemes shows significant improvement compared to previous studies. Perfect classification (100% accuracy) is achieved for the 3-tap multipath channel at Signal-to-Noise Ratio (SNR) values exceeding 6 dB, and for the 4-tap multipath channel at SNR values above 7 dB.

关键词： Automatic modulation classification Discrimination function Higher-order cumulants Multipath fading channel feature selection algorithms

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Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khalvati, Bagher Kavousi, Kaveh Keyhanipour, Amir Hosein Arabfard, Masoud Department of Bioinformatics Kish International Campus University of Tehran Kish Iran Laboratory of Complex Biological Systems and Bioinformatics (CBB) Department of Bioinformatics Institute of Biochemistry and Biophysics (IBB) University of Tehran Tehran Iran Computer Engineering Department Faculty of Engineering College of Farabi University of Tehran Tehran Iran Chemical Injuries Research Center Systems Biology and Poisonings Institute Baqiyatallah University of Medical Sciences Tehran Iran

Objective: Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process. Methods: We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance. Results: Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively. Conclusion: This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs. © The Author(s) 2025.

关键词： Biomarker Bronchogenic carcinoma feature selection algorithms Information fusion Machine learning

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