Results 261 to 270 of about 371,575 (291)

Text filtering by boosting naive Bayes classifiers

Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000
Several machine learning algorithms have recently been used for text categorization and filtering. In particular, boosting methods such as AdaBoost have shown good performance applied to real text data. However, most of existing boosting algorithms are based on classifiers that use binary-valued features.
Yu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang   +2 more
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An Improved Naive Bayes Classifier on Imbalanced Attributes

International Journal of Organizational and Collective Intelligence, 2019
Data plays a major and prominent role in this modern information era. Classification is a data mining task to discover the hidden information from large amounts of data stored in the repository. This process becomes extremely challenging in case of highly imbalanced dataset.
S. Geetha 0001, R. Maniyosai
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A Focused Crawler Based on Naive Bayes Classifier

2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 2010
The exponential growth of information on the World Wide Web makes it increasingly difficult to discover relevant data about a specific topic. In this case, growing interest is emerging in focused crawler, a program that traverses the Internet by choosing relevant pages to a predefined topic and neglecting those out of concern.
Wenxian Wang, Xingshu Chen, Yongbin Zou, Haizhou Wang 0001, Zongkun Dai   +4 more
openaire   +1 more source

Prediction of conotoxin superfamilies by the Naive Bayes classifier

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017
Conotoxins are a group of high specialized and functionally diverse peptides. Because the conotoxins are selectivity for membrane receptors and ion channels, so they can make valuable biological probes and drug targets. Successful prediction of the conotoxin superfamily peptide has important biological meaning in the pharmacology of the neurotoxins. In
Haiyan Huo, Lei Yang
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An Implementation of Naive Bayes Classifier

2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018
The necessity of classification is highly demanded in real life. As a mathematical classification approach, the Naive Bayes classifier involves a series of probabilistic computations for the purpose of finding the best-fitted classification for a given piece of data within a problem domain.
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Mixture of latent multinomial naive Bayes classifier

Applied Soft Computing, 2018
Abstract Naive Bayes classifier has been extensively applied in various domains in the past few decades due to its simple structure and remarkable predictive performance. However, it is based on a strong assumption which confines its usage for many real-world applications; conditional independence of attributes given class information. In this paper,
Nima Shiri Harzevili, Sasan Hossein Alizadeh   +1 more
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A Semi-naive Bayes Classifier with Grouping of Cases

2007
In this work, we present a semi-naive Bayes classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged.
Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral   +3 more
openaire   +1 more source

The Application of Naive Bayes Classifier in Name Disambiguation

2017
Name repetition exists in the academic resource management system, which brings difficulties to academic evaluation, information retrieval, citation analysis and so on. According as different authors use function words in different habits, the Naive Bayes classifier was used to study in this paper.
Na Li, Jin Han
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Naive Bayes Classifier for Positive Unlabeled Learning with Uncertainty

Proceedings of the 2010 SIAM International Conference on Data Mining, 2010
Existing algorithms for positive unlabeled learning (PU learning) only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. In this paper, based on positive naive Bayes (PNB), which is a PU learning algorithm for certain data, we propose an ...
Jiazhen He, Yang Zhang 0010, Xue Li 0001, Yong Wang   +3 more
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One Generalization of the Naive Bayes to Fuzzy Sets and the Design of the Fuzzy Naive Bayes Classifier

2005
Despite its unrealistic independence assumption, the Naive Bayes classifier is remarkably successful in practice. In the Naive Bayes classifier, all variables are assumed to be nominal variables, it means that each variable has a finite number of values.
Jiacheng Zheng, Yongchuan Tang
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