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Research on Classification of Scientific and Technological Documents Based on Naive Bayes

Authors:

Hong Zhang,

Hanshuo Wei,

Yeye Tang,

Qiumei PuAuthors Info & Claims

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

Pages 327 - 331

https://doi.org/10.1145/3318299.3318330

Published: 22 February 2019 Publication History

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Abstract

Text classification is an important step for text mining in the direction of data mining. Today, text categorization techniques are widely used in various fields, such as user behavior analysis in shopping recommendation systems, and spam filtering, but text categories based on scientific literature are seldom studied. This article uses biological material information. The scientific literature of the aspect is text, and the naive Bayesian method is used to classify the literature into different topic types. It is evaluated through the model test standard in data mining to verify the validity of the method. Finally, the research trend of biological materials A simple analysis was performed.

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Ahmed ZMittal SSingh H(2021)Text Clustering Techniques for Voice of Customer AnalysisCongress on Intelligent Systems10.1007/978-981-33-6981-8_57(725-735)Online publication date: 28-May-2021
https://doi.org/10.1007/978-981-33-6981-8_57
Usha GNarang MKumar A(2021)Detection and Classification of Distributed DoS Attacks Using Machine LearningComputer Networks and Inventive Communication Technologies10.1007/978-981-15-9647-6_78(985-1000)Online publication date: 3-Jun-2021
https://doi.org/10.1007/978-981-15-9647-6_78
Jain DJain RUpadhyay YKathuria ALan X(2019)Deep Refinement: capsule network with attention mechanism-based system for text classificationNeural Computing and Applications10.1007/s00521-019-04620-zOnline publication date: 3-Dec-2019
https://doi.org/10.1007/s00521-019-04620-z

Index Terms

Research on Classification of Scientific and Technological Documents Based on Naive Bayes
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

February 2019

563 pages

ISBN:9781450366007

DOI:10.1145/3318299

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Southwest Jiaotong University

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Association for Computing Machinery

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Published: 22 February 2019

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ICMLC '19: 2019 11th International Conference on Machine Learning and Computing

February 22 - 24, 2019

Zhuhai, China

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Ahmed ZMittal SSingh H(2021)Text Clustering Techniques for Voice of Customer AnalysisCongress on Intelligent Systems10.1007/978-981-33-6981-8_57(725-735)Online publication date: 28-May-2021
https://doi.org/10.1007/978-981-33-6981-8_57
Usha GNarang MKumar A(2021)Detection and Classification of Distributed DoS Attacks Using Machine LearningComputer Networks and Inventive Communication Technologies10.1007/978-981-15-9647-6_78(985-1000)Online publication date: 3-Jun-2021
https://doi.org/10.1007/978-981-15-9647-6_78
Jain DJain RUpadhyay YKathuria ALan X(2019)Deep Refinement: capsule network with attention mechanism-based system for text classificationNeural Computing and Applications10.1007/s00521-019-04620-zOnline publication date: 3-Dec-2019
https://doi.org/10.1007/s00521-019-04620-z

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Naive Bayes for optimal ranking

Averaged Naive Bayes Trees: A New Extension of AODE

Deep feature weighting for naive Bayes and its application to text classification

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