scholarly journals A method for avoiding bias from feature selection with application to naive Bayes classification models

2008 ◽  
Vol 3 (1) ◽  
pp. 171-196 ◽  
Author(s):  
Longhai Li ◽  
Jianguo Zhang ◽  
Radford M. Neal
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

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

Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity

2017 ◽  
Vol 41 ◽  
pp. 56-63 ◽  
Author(s):  
Hui Zhang ◽  
Yan-Li Kang ◽  
Yuan-Yuan Zhu ◽  
Kai-Xia Zhao ◽  
Jun-Yu Liang ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

scholarly journals Deteksi Komentar Cyberbullying Pada Media Sosial Berbahasa Inggris Menggunakan Naïve Bayes Classification

2020 ◽  
Vol 7 (1) ◽  
pp. 46-54
Author(s):  
Jasman Pardede
Keyword(s):  
Feature Selection ◽  
Conditional Probability ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classification ◽  
Text Preprocessing ◽  
Naïve Bayes Classification

Pesatnya perkembangan teknologi dan media sosial dapat memudahkan pengguna untuk menyampaikan informasi. Selain itu, media sosial juga memberikan dampak negatif dengan cara memposting tulisan kejam atau berkomentar semena-mena tanpa memikirkan akibat pada orang lain. Hal inilah yang menjadikan salah satu terjadinya tindak kekerasan dalam dunia maya (Cyberbullying). Tahapan awal yang dilakukan dalam penelitian ini adalah pengolahan bahasa atau yang disebut dengan text preprocessing meliputi tokenizing,casefolding, stopword removal dan stemming. Kemudian feature selection yaitu mengubah dokument teks menjadi matriks dengan tujuan untuk mendapatkan fitur pada setiap kata untuk dijadikan parameter atau kriteria klasifikasi. Untuk pengambilan keputusan apakah komentar mengandung makna bully atau nonbully menggunakan algoritma Naïve Bayes Classification dengan model multinomial naïve bayes. Perhitungan yang dilakukan adalah menghitung nilai probabilitas setiap kata yang muncul berdasarkan classdan nilai perkalian class conditional probability. Berdasarkan hasil eksperimen menggunakan dataset “cyberbullying comments” yang diambil dari Kaggle  akurasi yang didapat sebesar 80%, precission 81% dan recall 80%.

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

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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