Comparing SVM and Naïve Bayes Classifier for Fake News Detection

Nurhasanah, Nurhasanah; Sumarly, Daniel Emerald; Pratama, Jason; Heng, Ibrahim Tan Kah; Irwansyah, Edy

doi:10.21512/emacsjournal.v4i3.8670

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Training data can be used to calculate P(C = c) . Multiplying feature conditional probabilities and prior probabilities classifies new data into the most likely class 46 .…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Islam,

Sheakh,

Tahosin

et al. 2024

Sci Rep

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Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

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“…Training data can be used to calculate P(C = c) . Multiplying feature conditional probabilities and prior probabilities classifies new data into the most likely class 46 .…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Islam,

Sheakh,

Tahosin

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This stage assessed the performance of the algorithms by employing a confusion matrix [99], which formed tabulated data indicating the amount of correct or incorrect classifications, according to the decision by the algorithm. Technically, the confusion matrix captured critical information on the binary comparison between the actual categorizations and those predicted by the classification algorithms [100]. It could manifest as instances where data belonging to category "a" were incorrectly predicted as category "b".…”

Section: Confusion Matrixmentioning

confidence: 99%

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

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Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.

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Comparing SVM and Naïve Bayes Classifier for Fake News Detection

Cited by 2 publications

References 16 publications

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

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