A pipeline and comparative study of 12 machine learning models for text classification

Occhipinti, Annalisa; Rogers, Louis; Angione, Claudio

doi:10.1016/j.eswa.2022.117193

Cited by 54 publications

(20 citation statements)

References 50 publications

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“…The polynomial kernel SVM is based on a similar approach to the linear kernel. In the kernel polynomials depend not only on one particular feature of the input sample, but also on their combination in determining their similarity [42]. Comparison of the accuracy results obtained by polynomial kernel with γ=1, 2, and 3 can be seen in the following Table 6-8.…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

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Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.

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Section: Resultsmentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

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“…Many recent studies utilized the Enron, Lingspam, and PU datasets. As shown in Table 2, the use of Enron dataset has been reported in several studies between the years 2020 and 2022 [11,13,15,26,27,41]. Similarly, the use of Lingspam and PU has also been reported in two recent studies published in 2021 [11,15].…”

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confidence: 75%

“…The performance is measured using accuracy, precision, recall, F-score, and other metrics. Occhipinti et al [41] employ twelve machine learning algorithms which include K-NN, Xboost, and several variants of regression, SVM, and NB. The performance is evaluated using the Enron dataset while using recall, precision, F-score, and a couple of other metrics.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

et al. 2022

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The increasing volume of unsolicited bulk emails has become a major threat to global security. While a significant amount of research has been carried out in terms of proposing new and better algorithms for email spam detection, relatively less attention has been given to evaluation metrics. Some widely used metrics include accuracy, recall, precision, and F-score. This paper proposes a new evaluation metric based on the concepts of fuzzy logic. The proposed metric, termed μO, combines accuracy, recall, and precision into a multi-criteria fuzzy function. Several possible evaluation rules are proposed. As proof of concept, a preliminary empirical analysis of the proposed scheme is carried out using two models, namely BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long short-term memory) from the domain of deep learning, while utilizing three benchmark datasets. Results indicate that for the Enron and PU datasets, LSTM produces better results of μO, with the values in the range of 0.88 to 0.96, whereas BERT generates better values of μO in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric.

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“…SLISEMAP [179] finds local Explanations for all data items and builds a (typically) twodimensional global visualization of the black box model such that data items with similar Local Explanations are projected nearby. [180], [181] are two works focused on text classification that use Spam datasets.…”

Section: ) Explainable Artificial Intelligence In Phishing and Spam D...mentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

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A pipeline and comparative study of 12 machine learning models for text classification

Cited by 54 publications

References 50 publications

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

Explainable Artificial Intelligence in CyberSecurity: A Survey

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