Multinomial Naive Bayes for Text Categorization Revisited
By Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, G. Holmes. Australian Conference on Artificial Intelligence, 2004
497 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/0594c62529549fae2c91909539c4df8682fce442
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Todd Kulesza
- Simone Stumpf
- Margaret M. Burnett
- Weng-Keen Wong
- Li Yang
- Abdallah Shami
- Zach Jorgensen
- V. Varadharajan
In Information Systems
- Li Yang
- Zach Jorgensen
- V. Varadharajan
- Margaret M. Burnett
- Nando de Freitas
- Abdallah Shami
- Simone Stumpf
- Yan Zhou
In Molecular Biology
Citations in the corpus
- Data Mining Applications Using Artificial Adaptive Systems (2012, background): “IEEE Trans Nucl Sci 45:2132–2138 Kohonen T (1995–2001) Self-organizing map, 3rd edn. Springer, Berlin Kosko B (1992) Neural networks and fuzzy systems.”
- Meta Net: A New Meta-Classifier Family (2013, unknown): “Multinomial Naı̈ve Bayes (MNB) (Kibriya et al. 2005), 19.”
- A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “Feature vectors in these values represent the number of occurrences in which this event occurs [230].”
- A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters (2008, methodology): “Note that tf-idf weighting is widely used in information retrieval and text mining, and has been shown to be able to greatly improve the performance of multinomial naı̈ve Bayes in several text categorization tasks (Kibriya et al., 2004).”
- Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015, methodology): “An MNB classifier computes the probability that a given input (e.g., the document being classified) has of belonging to each output (e.g., the possible labels).”
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “There are three Naive Bayes (NB) algorithms: Gaussian Naive Bayes [89], Bernoulli Naive Bayes [90] and Multinomial Naive Bayes [91].”
- A Machine Learning Perspective on Predictive Coding with PAQ (2011, background): “The four best results (including PAQclass) in Table 8 (Marton et al., 2005, Weinberger and Saul, 2009, Kibriya et al., 2005) all seem to use the same version of 20news.”
- A Machine Learning Perspective on Predictive Coding with PAQ8 (2012, background): “35 MULTINOMIAL NAIVE BAYES + TFIDF [19] 80-20 TRAIN-TEST SPLIT 93.”
- On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “Multinomial NB [50] is designed for multinomially-distributed data based on the nave Bayes algorithm.”