scholarly journals A Comparative Study of Centroid-Based and Naïve Bayes Classifiers for Document Categorization

2017 ◽  
Vol 07 (03) ◽  
pp. 59-63
Author(s):  
Rupali P. Patil
Keyword(s):  
Comparative Study ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Document Categorization

scholarly journals Energy Management in Wireless Sensor Networks Based on Naive Bayes, MLP, and SVM Classifications: A Comparative Study

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Abdulaziz Y. Barnawi ◽  
Ismail M. Keshta
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Comparative Study ◽  
Energy Management ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Multilayer Perceptrons

Maximizing wireless sensor networks (WSNs) lifetime is a primary objective in the design of these networks. Intelligent energy management models can assist designers to achieve this objective. These models aim to reduce the number of selected sensors to report environmental measurements and, hence, achieve higher energy efficiency while maintaining the desired level of accuracy in the reported measurement. In this paper, we present a comparative study of three intelligent models based on Naive Bayes, Multilayer Perceptrons (MLP), and Support Vector Machine (SVM) classifiers. Simulation results show that Linear-SVM selects sensors that produce higher energy efficiency compared to those selected by MLP and Naive Bayes for the same WSNs Lifetime Extension Factor.

A Comparative Study of Bug Classification Algorithms

2014 ◽  
Vol 24 (01) ◽  
pp. 111-138 ◽  
Author(s):  
Naresh Kumar Nagwani ◽  
Shrish Verma
Keyword(s):  
Comparative Study ◽  
Naive Bayes ◽  
Rbf Neural Network ◽  
Naïve Bayes ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Software Bugs ◽  
The Comparative Study ◽  
Bug Repositories ◽  
F Measure

The performance of ten classic algorithms to classify the software bugs for different bug repositories are compared. The algorithms included in the study are Naïve Bayes, Naïve Bayes Multinomial, Discriminative Multinomial Naïve Bayes (DMNB), J48, Support Vector Machine, Radial Basis Function (RBF) Neural Network, Classification using Clustering, Classification using Regression, Adaptive Boosting (AdaBoost) and Bagging. These algorithms are applied on four open source bug repositories namely Android, JBoss-Seam, Mozilla and MySql. The classification is evaluated using 10-fold cross validation technique. The accuracy and F-measure parameters are compared for all of the algorithms. The concept of software bug taxonomy hierarchy is also introduced with eleven standard bug categories (classes). The comparative study also covers the effect of number of categories over performance of classifiers in terms of accuracy and F-measure. The results are produced in tabular and graphical forms.

scholarly journals A Comparative Approach of Dimensionality Reduction Techniques in Text Classification

2019 ◽  
Vol 9 (6) ◽  
pp. 4974-4979 ◽  
Author(s):  
S. Rahamat Basha ◽  
J. K. Rani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Comparative Approach ◽  
Bayes Classifier ◽  
Average Precision ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Document Categorization ◽  
Feature Selection Techniques

This work deals with document classification. It is a supervised learning method (it needs a labeled document set for training and a test set of documents to be classified). The procedure of document categorization includes a sequence of steps consisting of text preprocessing, feature extraction, and classification. In this work, a self-made data set was used to train the classifiers in every experiment. This work compares the accuracy, average precision, precision, and recall with or without combinations of some feature selection techniques and two classifiers (KNN and Naive Bayes). The results concluded that the Naive Bayes classifier performed better in many situations.

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