Text Classification Based on Support Vector Machine

Nguyện, Lê Thị Minh

doi:10.37569/dalatuniversity.9.2.536(2019)

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Neural network (Van, 2017) that data collected from electronic news sites such as Vnexpress.net, Tuoitre.vn, Thanhnien.vn, and Nld.com.vn with classification accuracy of 99.75%. Specifically, it is mentioned that Vietnamese text classification with SVM (Nguyen, 2019) using data collected from the electronic page Vnexpress.net, Tuoitre.vn, Thanhnien.vn, Teleport-pro.softonic.com, and Nld.com.vn with classification accuracy 94%.…”

Section: Related Workmentioning

confidence: 99%

Identify and predict incorrect prices by Machine Learning Model

Lam,

Nguyen,

Nguyen

2023

CTU J. of Inn. & Sus. Dev.

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Electronic commerce (e-commerce) brings huge advantages to businesses for selling products through multiple online shops. However, companies have difficulties in supervising the prices of products set by different retail shops on e-commerce platforms. Addressing these difficulties, we suggest a method to identify and predict products that sell at incorrect prices using a machine learning model combined price analysis. The study uses four machine learning models: K-nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multinomial Naive Bayes (MNB) and two text-based information extraction methods: BoW and TF-IDF to find to the best method. The research results show that the RF model and text-based information extraction method by the BoW provide more average accuracy than other specific models, when experimenting on the filter dataset the average accuracy after 10 runs are RF: 98.06%, SVM: 83.92%, MNB: 92.21%, KNN: 94.06%. Experimental results on the product dataset have an accuracy of RF: 83.02%, SVM: 55%, MNB: 79.33%, KNN: 79.36%.

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Section: Related Workmentioning

confidence: 99%

Identify and predict incorrect prices by Machine Learning Model

Lam,

Nguyen,

Nguyen

2023

CTU J. of Inn. & Sus. Dev.

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Designing an information system for the electronic document management of a university: automatic classification of documents

Tkachenko

Denisova

2022

J. Phys.: Conf. Ser.

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To ensure the effective functioning of the university educational environment, document flow processes automation, which includes the task of documents automatic classification, is of great importance. The article considers the task of classifying university documents by machine learning methods in order to improve the quality of classification. Documents preprocessing was carried out, which made it possible to distinguish significant words in documents, due to which the accuracy of documents classification increased. Described are methods of extracting features from text TF and TF-IDF, which determine keywords by words frequency included in document. A modification of the TF-IDF method is proposed, which consists in calculating the words importance depending on their part of speech. This made it possible to improve the classification quality by highlighting only important and significant words in documents. Suggested is a classification algorithm using a method of support vectors to reduce the documents number involved in classification and a method of k-nearest neighbor for classification. The advantage of this algorithm over the described analogues is shown, which is expressed in the number of mistakenly classified documents decrease.

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Wetland Insect Image Recognition System Based on SVM-AdaBoost Machine Learning Model

Luo

Zhang

et al. 2019

2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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Text Classification Based on Support Vector Machine

Cited by 3 publications

References 12 publications

Identify and predict incorrect prices by Machine Learning Model

Identify and predict incorrect prices by Machine Learning Model

Designing an information system for the electronic document management of a university: automatic classification of documents

Wetland Insect Image Recognition System Based on SVM-AdaBoost Machine Learning Model

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