A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms

Endut, Nurshahira; Hamzah, Wan Mohd Amir Fazamin Wan; Ismail, Ismahafezi; Yusof, Mohd Kamir; Baker, Yousef Abu; Yusoff, Hafiz

doi:10.18421/tem112-20

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…This aims to obtain highly predictive performance with reasonable computational complexity. Exploring the MLC review research in Reference 20, the authors show that SVM was the most often employed method in ML algorithms, followed by Decision Tree (DT), Logistic Regression (LR), K‐Nearest Neighbors, Random Forest, and Naïve Bayes. According to this latter research, the SVM had the greatest number of repetitions used to find the optimum accuracy for the MLC, with 23.…”

Section: Arabic Aspect Category Classificationmentioning

confidence: 99%

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

Ameur,

Hamdi,

Yahia

2023

Computational Intelligence

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In many fields, like aspect category detection (ACD) in aspect‐based sentiment analysis, it is necessary to label each instance with more than one label at the same time. This study tackles the multilabel classification problem in the ACD task for the Arabic language. For this purpose, we used Arabic hotel reviews from the SemEval‐2016 dataset, comprising 13,113 annotated tuples provided for training (10,509) and testing (2,604). To extract valuable information, we first propose specific data preprocessing. Then, we suggest using the dynamic weighted loss function and a data augmentation method to fix the problem with this dataset's imbalance. Using two possible approaches, we develop new ways to find different categories of things in a review sentence. The first is based on classifier chains using machine learning models. The second is based on transfer learning using pretrained AraBERT fine‐tuning for contextual representation. Our findings show that both approaches outperformed the related works for ACD on the Arabic SemEval‐2016. Moreover, we observed that AraBERT fine‐tuning performed much better and achieved a promising ‐score of .

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Section: Arabic Aspect Category Classificationmentioning

confidence: 99%

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

Ameur,

Hamdi,

Yahia

2023

Computational Intelligence

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“…Classification is a machine learning approach in data mining that is often used where many methods are chosen to classify a dataset [1], [2]. Classifications that involve two classes are called binary classifications [3], [4], while those that involve more than two classes are called multi-class classifications [5]- [7]. In real applications, classification techniques are needed such as medical disease analysis, text classification, user smartphone classification, and images [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Dani,

Artanta Ginting

2024

IJECE

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In this study, we apply two classification algorithm methods, namely the Gaussian naïve Bayes (GNB) and the decision tree (DT) classifiers. The Gaussian naïve Bayes classifier is a probability-based classification model that predicts future probabilities based on past experiences. Whereas the decision tree classifier is based on a decision tree, a series of tests that are performed adaptively where the previous test affects the next test. Both of these methods are simulated on the Iris dataset where the dataset consists of three types of Iris: setosa, virginica, and versicolor. The data is divided into two parts, namely training and testing data, in which there are several features as information on flower characteristics. Furthermore, to evaluate the performance of the algorithms on both methods and determine the best algorithm for the dataset, we evaluate it using several metrics on the training and testing data for each method. Some of these metrics are recall, precision, F1-score, and accuracy where the higher the value, the better the algorithm's performance. The results show that the performance of the decision tree classifier algorithm is the most outperformed on the Iris dataset.

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“…In machine learning, varieties of algorithms such as supervised, unsupervised, semi-supervised, reinforcement, and transduction, are frequently employed. Supervised learning is the ability of an algorithm to synthesize knowledge from previously labelled data in order to predict future unlabelled cases [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Zaini¹,

Awang²

2023

IJACSA

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In cardiology, as in other medical specialties, early and accurate diagnosis of heart disease is crucial as it has been the leading cause of death over the past few decades. Early prediction of heart disease is now more crucial than ever. However, the state-of-the-art heart disease prediction strategy put more emphasis on classifier selection in enhancing the accuracy and performance of heart disease prediction, and seldom considers feature reduction techniques. Furthermore, there are several factors that lead to heart disease, and it is critical to identify the most significant characteristics in order to achieve the best prediction accuracy and increase prediction performance. Feature reduction reduces the dimensionality of the information, which may allow learning algorithms to work quicker and more efficiently, producing predictive models with the best rate of accuracy. In this study, we explored and suggested a hybrid of two distinct feature reduction techniques, chi-squared and analysis of variance (ANOVA). In addition, using the ensemble stacking method, classification is performed on selected features to classify the data. Using the optimal features based on hybrid features combination, the performance of a stacking ensemble based on logistic regression yields the best result with 93.44%. This can be summarized as the feature selection method can take into account as an effective method for the prediction of heart disease.

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A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms

Cited by 7 publications

References 22 publications

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

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