Naive Bayes: Theory

2018 ◽  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Theory

scholarly journals IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

2020 ◽  
Vol 3 (2) ◽  
pp. 110-117
Author(s):  
Irayori Loelianto ◽  
Moh. Sofyan S Thayf ◽  
Husni Angriani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Design Stage ◽  
Prospective Students ◽  
Classifier Design ◽  
Python Programming Language ◽  
Bayes Theory ◽  
Python Programming

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.

The study of traffic flow model based on cellular automata and Naive Bayes

2019 ◽  
Vol 30 (05) ◽  
pp. 1950034 ◽  
Author(s):  
Hong Zhang ◽  
Juan Wei ◽  
Xiaoling Gao ◽  
Jun Hu
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Average Velocity ◽  
Flow Model ◽  
Naive Bayes ◽  
Maximum Velocity ◽  
Naïve Bayes ◽  
Flow State ◽  
Traffic Flow Model ◽  
Bayes Theory

A new traffic flow model is proposed based on cellular automata and Naive Bayes theory to effectively describe the traffic flow velocity and flow state of the road. On the basis of NaSch model, the safety distance is fully considered in this model, and random deceleration and inflow rules of a vehicle are introduced. At the same time, vehicle acceleration, deceleration and lane change are optimized with Naive Bayes theory. Finally, experimental platform is used for numerical analysis, and the relationship between such parameters as average velocity of traffic flow, maximum velocity of traffic flow, number of inflow vehicles and random deceleration probability, etc. is studied in depth. The results show that the maximum velocity has a great effect on the traffic flow state, and when the vehicle inflow probability is lower, random deceleration probability has less effect on the average velocity and the number of vehicles waiting for inflow; on the contrary, the higher the random deceleration probability is, the more obvious the tendency of road congestion.

scholarly journals Improving a SVM Meta-classifier for Text Documents by using Naive Bayes

2010 ◽  
Vol 5 (3) ◽  
pp. 351 ◽  
Author(s):  
Daniel Morariu ◽  
Radu Crețulescu ◽  
Lucian Vințan
Keyword(s):  
Classification Accuracy ◽  
Text Categorization ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Experimental Results ◽  
Text Documents ◽  
Text Document ◽  
Bayes Theory ◽  
Individual Classifier ◽  
Component Classifier

Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated two approaches: a) to develop a classifier for text document based on Naive Bayes Theory and b) to integrate this classifier into a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a meta-classifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the classification accuracy and that our improved meta-classification strategy gives better results than each individual classifier. For Reuters2000 text documents we obtained classification accuracies up to 93.87%

Criminal prediction using Naive Bayes theory

2016 ◽  
Vol 28 (9) ◽  
pp. 2581-2592 ◽  
Author(s):  
Mehmet Sait Vural ◽  
Mustafa Gök
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Theory

scholarly journals Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 432
Author(s):  
Huiliang Wang ◽  
Hongfa Wang ◽  
Zening Wu ◽  
Yihong Zhou
Keyword(s):  
Urban Areas ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Extreme Weather Events ◽  
Urban Flooding ◽  
Urban Flood ◽  
Flood Depth ◽  
Depth Prediction ◽  
Bayes Theory ◽  
Flooded Areas

With global warming, the number of extreme weather events will increase. This scenario, combined with accelerating urbanization, increases the likelihood of urban flooding. Therefore, it is necessary to predict the characteristics of flooded areas caused by rainstorms, especially the flood depth. We applied the Naive Bayes theory to construct a model (NB model) to predict urban flood depth here in Zhengzhou. The model used 11 factors that affect the extent of flooding—rainfall, duration of rainfall, peak rainfall, the proportion of roads, woodlands, grasslands, water bodies and building, permeability, catchment area, and slope. The forecast depth of flooding from the NB model under different rainfall conditions was used to draw an urban inundation map by ArcGIS software. The results show that the probability and degree of urban flooding in Zhengzhou increases significantly after a return period of once every two years, and the flooded areas mainly occurred in older urban areas. The average root mean square error of prediction results was 0.062, which verifies the applicability and validity of our model in the depth prediction of urban floods. Our findings suggest the NB model as a feasible approach to predict urban flood depth.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

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