scholarly journals Performance Comparison and Optimization of Text Document Classification using k-NN and Naïve Bayes Classification Techniques

2017 ◽  
Vol 116 ◽  
pp. 107-112 ◽  
Author(s):  
Zulfany Erlisa Rasjid ◽  
Reina Setiawan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Document Classification ◽  
Performance Comparison ◽  
Classification Techniques ◽  
Text Document ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Text Document Classification

scholarly journals Exploring multinomial naïve Bayes for Yorùbá text document classification

2020 ◽  
Vol 39 (2) ◽  
pp. 528-535
Author(s):  
I.I. Ayogu
Keyword(s):  
English Language ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Document Classification ◽  
Text Documents ◽  
Bayes Model ◽  
Text Document ◽  
Text Document Classification ◽  
Yoruba Language ◽  
Language Text

The recent increase in the emergence of Nigerian language text online motivates this paper in which the problem of classifying text documents written in Yorùbá language into one of a few pre-designated classes is considered. Text document classification/categorization research is well established for English language and many other languages; this is not so for Nigerian languages. This paper evaluated the performance of a multinomial Naive Bayes model learned on a research dataset consisting of 100 samples of text each from business, sporting, entertainment, technology and political domains, separately on unigram, bigram and trigram features obtained using the bag of words representation approach. Results show that the performance of the model over unigram and bigram features is comparable but significantly better than a model learned on trigram features. The results generally indicate a possibility for the practical application of NB algorithm to the classification of text documents written in Yorùbá language. Keywords: Supervised learning, text classification, Yorùbá language, text mining, BoW Representation

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

2016 ◽  
Vol 97 ◽  
pp. 141-149 ◽  
Author(s):  
Hui Zhang ◽  
Zhi-Xing Cao ◽  
Meng Li ◽  
Yu-Zhi Li ◽  
Cheng Peng
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

scholarly journals Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875603 ◽  
Author(s):  
Yao-Hua Ho ◽  
Yu-Te Huang ◽  
Hao-Hua Chu ◽  
Ling-Jyh Chen
Keyword(s):  
Environmental Changes ◽  
Naive Bayes ◽  
Cost Effective ◽  
Parameter Tuning ◽  
Naïve Bayes ◽  
Spatial Distance ◽  
Environmental Sensors ◽  
Adaptive Sensing ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-to-home sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification–enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning–based approaches.

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