Enhancing Performance of naïve bayes in text classification by introducing an extra weight using less number of training examples

Shathi, Shahnaj Parvin; Hossain, Md. Delowar; Nadim, Md; Riayadh, Sayed Golam Rasul; Sultana, Tangina

doi:10.1109/iwci.2016.7860355

Cited by 7 publications

(1 citation statement)

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“…Two types of models that the model could describe are Bernoulli and multinomial. The performance and improvement of the Naive Bayes classifier is a naive assumption for text classification matching the performance enhancement (Shathi et al, 2016 ).…”

Section: Related Workmentioning

confidence: 99%

An efficient approach for textual data classification using deep learning

Alqahtani

Khan

Alsubai

et al. 2022

Front. Comput. Neurosci.

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Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

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Section: Related Workmentioning

confidence: 99%