Some Effective Techniques for Naive Bayes Text Classification

By Sang-Bum Kim, Kyoung-Soo Han, Hae-Chang Rim, Sung-Hyon Myaeng. IEEE Transactions on Knowledge and Data Engineering, 2006

565 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/4686d10750167d9c578392f645fdaa4a55197ffb

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Citations in the corpus

A Similarity Measure for Text Classification and Clustering (2014, background): “Such highdimensionality and sparsity can be a severe challenge for similarity measure which is an important operation in text processing algorithms [5], [6], [10], [11], [23], [24], [28], [30], [33], [34], [37], [41], [42], [47], [49], [50].”
TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “They [19] propose Poisson model for NB text classification and also give weight enhancing method to improve the performance of rare categories.”
HDLTex: Hierarchical Deep Learning for Text Classification (2017, methodology): “The naı̈ve Bayes document classifier used for this study uses word-level classification [11].”
Text Classification Algorithms: A Survey (2019, methodology): “This model is used as baseline of many papers which is word-level of Naïve Bayes classifier [3,130] as follows:”
A Review of Machine Learning Algorithms for Text-Documents Classification (2010, methodology): “The authors in [85] propose a Poisson Naive Bayes text classification model with weightenhancing method, and shows that the new model assumes that a document is generated by a multivariate Poisson model.”
Feature selection for text classification: A review (2018, methodology): “Some effective techniques are proposed to further improve prediction accuracy of the multinomial NB classifiers [47].”
Content-based Recommender Systems: State of the Art and Trends (2011, background): “In [46], the authors propose a multivariate Poisson model for naı̈ve Bayes text classification that allows more reasonable parameter estimation under the above mentioned conditions.”
Graph-based term weighting for text categorization (2015, background): “a) Text classification: TC is one of the most fundamental and well studied tasks in text analytics and a number of diverse approaches have been proposed [21], [35], [14], [15], [22], [37], [26], [16], [19].”
Machine Learning Methods in Drug Discovery (2020, background): “These techniques enhance the accuracy of retrieved data sets, which generally originate in large, muddled sources [45,46].”
A brief survey on sequence classification (2010, methodology): “The widely used methods for document classiflcation include Naive Bayes [ 29 ] and SVM [43].”
Long Document Classification From Local Word Glimpses via Recurrent Attention Learning (2019, methodology): “Traditional methods such as bag of words [3] and n-grams [4] use statistical methods to represent the document as N-dimensional feature vectors and then classify these features via SVM [5], [6] or Naive Bayes [7], [8].”