scholarly journals Robust Method of Sparse Feature Selection for Multi-Label Classification with Naive Bayes

2014 ◽  
Author(s):  
Dymitr Ruta
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Robust Method ◽  
Selection For ◽  
Sparse Feature Selection

Feature selection for text classification with Naïve Bayes

2009 ◽  
Vol 36 (3) ◽  
pp. 5432-5435 ◽  
Author(s):  
Jingnian Chen ◽  
Houkuan Huang ◽  
Shengfeng Tian ◽  
Youli Qu
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Selection For

scholarly journals Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

2020 ◽  
Vol 1641 ◽  
pp. 012085
Author(s):  
Dwi Andini Putri ◽  
Dinar Ajeng Kristiyanti ◽  
Elly Indrayuni ◽  
Acmad Nurhadi ◽  
Denda Rinaldi Hadinata
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Selection For ◽  
Bayes Algorithm

Feature selection for multi-label naive Bayes classification

2009 ◽  
Vol 179 (19) ◽  
pp. 3218-3229 ◽  
Author(s):  
Min-Ling Zhang ◽  
José M. Peña ◽  
Victor Robles
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classification ◽  
Selection For ◽  
Naïve Bayes Classification

Privacy Preserving Feature Selection for Vertically Distributed Medical Data based on Genetic Algorithms and Naïve Bayes

2018 ◽  
Vol 9 (3) ◽  
pp. 1-22
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Naive Bayes ◽  
Original Data ◽  
Naïve Bayes ◽  
Medical Data ◽  
Patient Privacy ◽  
Useful Knowledge ◽  
Selection For ◽  
And Performance

Machine learning is a powerful tool to mine useful knowledge from vast databases. Many establishments in the medical area such as hospitals, laboratories want to join their efforts with the ambition to extract models that are more accurate. However, this approach faces problems. Due to the laws protecting patient privacy or other similar concerns, parties are reluctant to share their data. In vast amounts of data, which are useful and pertinent in constructing accurate data mining models? In this article, the researchers deal with these challenges for vertically distributed medical data. They propose an original secure wrapper solution to perform feature selection based on genetic algorithms and distributed Naïve Bayes. Contrary to the previous solutions, the original data is not perturbed. Therefore, the data utility and performance are preserved. They prove that the proposed solution selects relevant attributes to increase performance, preserving patient privacy.

Sign in / Sign up

Export Citation Format

Share Document