Evaluation of Naive Bayes and Support Vector Machines for Wikipedia

Mocherla, Sridhar; Danehy, Alexander; Impey, C.

doi:10.1080/08839514.2018.1440907

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…It is recommended that future studies explore the model using Deep Learning and test it on different datasets to determine its strengths and weaknesses. Furthermore, hybrid testing needs to be implemented on the algorithms used as demonstrated in previous studies such as SVM with Naï ve Bayes [59], SVM with KNN [60], and others.…”

Section: Discussionmentioning

confidence: 99%

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Zamsuri,

Defit,

Nurcahyo

2024

JAIT

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Emotion is an individual's response to an event or situation. Research related to emotions in the field of data science falls under sentiment analysis, where sentiment analysis mostly focuses on determining the positive or negative emotional tone. This study attempts to classify emotions into labels such as Happy, Love, Surprise, Anger, Fear, and Sadness. Then, these six emotion labels are further categorized into positive and negative groups. The dataset for this research comprises tweets from Twitter related to the 2024 presidential election in Indonesia. Several machine learning algorithms are employed in this study, including Naï ve Bayes (Multinomial Bayes, Bernoulli Bayes, Complement Bayes), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), comparing two feature extraction methods: Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). The resultsshow that the use of SVM does not yield better accuracy, whether using TF-IDF or BoW. Therefore, this study improves the accuracy of the SVM algorithm by combining kernels available in SVM (Polynomial, Radial Basis Function (RBF), and Linear). From the conducted experiments, it is evident that SVM with a combined kernel, referred to as SVM Polynomial, RBF, Linier (PoRLi) in this study, achieves better accuracy compared to a single kernel. This is reflected in the classification accuracy with 6 labels, reaching 62%, indicating a 2% increase from SVM Linear, which obtained the highest accuracy using a single kernel. Furthermore, in the classification of 2 labels, there is also a 1% increase when using SVM PoRLi. It can be concluded that SVM PoRLi can enhance accuracy across various labels.

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Section: Discussionmentioning

confidence: 99%

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Zamsuri,

Defit,

Nurcahyo

2024

JAIT

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“…This result difference was expected again as SVM is considered as a more robust algorithm for classification and has been reported to perform better than algorithms such as NB model in various complex applications. [40][41][42][43] Improving the data quality was another approach which was considered in this article so as to improve the accuracy results of the classification. The MGV is the most important of all the morphological features as it was the best input data variable which can be used to classify the inclusions.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods

Babu

Musi

Thiele

et al. 2022

steel research int.

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Nonmetallic inclusions have strong influence on final steel properties. An important characterization tool to make a comprehensive analysis of nonmetallic inclusions is the scanning electron microscope equipped with energy‐dispersive spectroscopy (SEM‐EDS). A major drawback which prevents its use for online‐steel assessment is the time taken for analysis. Machine learning methods have been previously introduced which circumvents the usage of the EDS for obtaining chemical information of the inclusion by classifying inclusion based on their back scatter electron images. This study introduces a method based on a simpler tabular data input consisting of morphological and mean gray values of inclusions. Naive Bayes and Support Vector Machine classifier models are built using the R statistical programming language. Two steel grades are considered for this study. The prediction results are shown to be satisfactory for both binary (maximum 89%) and 8‐inclusion class (maximum 61%) categorization. The input dataset is further improved by optimizing the image settings to distinguish the different types of nonmetallic inclusions. It is shown that this improvement results in a higher rate of correct predictions for both binary (maximum 98%) and 8‐class categorization (maximum 81%).

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“…where: P(c): probability of class c P(X): probability of the predictors X P(X|c): probability of having X features given class c P(c|X): probability of an instance X belonging to class c given the value of its dependent variables [29] d. Support vector machines; using a dataset of n features, a Support Vector Machine (SVM) attempts to find a decision boundary which maximises the margin between two observed classes [30]. This makes it a robust choice for binary classification.…”

Section: Text Classificationmentioning

confidence: 99%

Classifying lymphoma and tuberculosis case reports using machine learning algorithms

et al. 2021

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Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.

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Evaluation of Naive Bayes and Support Vector Machines for Wikipedia

Cited by 11 publications

References 6 publications

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods

Classifying lymphoma and tuberculosis case reports using machine learning algorithms

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