A Binary Approximate Naive Bayesian Classification Algorithm Based on SOM Neural Network Clustering

2017 ◽  
Author(s):  
Shen Honghong ◽  
He Lili
Keyword(s):  
Neural Network ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Network Clustering ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Som Neural Network

Study of Network Public Opinion Classification Method Based on Naive Bayesian Algorithm in Hadoop Environment

2014 ◽  
Vol 519-520 ◽  
pp. 58-61 ◽  
Author(s):  
Jian Xu ◽  
Bin Ma
Keyword(s):  
Public Opinion ◽  
Distributed Storage ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Single Server ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Bayes Algorithm ◽  
Network Public Opinion

In the light of the excellent distributed storage and parallel processing feature of hadoop cluster, a new kind of network public opinion classification method based on Naive Bayes algorithm in hadoop environment is studied. The collected public opinion documents are stored locally according to the HDFS architecture, and whose character words are extracted paralleled in Mapreduce process. Thus the naive Bayesian classification algorithm is parallel encapsulated on cloud computing platform. The MapReduce packaged Naive Bayesian classification algorithm performance is verified and the results show that the algorithm execution speed are significantly improved compared to a single server. Its public opinion classification accuracy rate is more than 85%, which can effectively improve the classification performance of network public opinion and classification efficiency.

scholarly journals Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Li Tiancheng ◽  
Ren Qing-dao-er-ji ◽  
Qiu Ying
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Inner Mongolia ◽  
Prediction Method ◽  
Classification Algorithm ◽  
Bayesian Algorithm ◽  
Naive Bayesian ◽  
Model Based ◽  
Naïve Bayesian

Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN KREDIT USAHA MIKRO PADA BANK MANDIRI GOMBONG

Respati ◽  
2017 ◽  
Vol 10 (30) ◽  
Author(s):  
Ika Nur Fajri ◽  
Bambang Soedijono W ◽  
Syamsul A Syahdan
Keyword(s):  
Neural Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayesian Classification ◽  
Naive Bayesian ◽  
Naïve Bayesian

ABSTRAKKetepatan dan kecepatan dalam mengambil keputusan menjadi suatu keharusan pada proses penentuan kredit sehingga akan banyak nasabah yang akan menerima hasil, apakah diterima atau ditolak pengajuan kreditnya, karena semakin banyak nasabah yang mengajukan kredit.Penelitian ini mengimplementasikan algoritma naïve bayes untuk membantu menentukan siapa yang berhak mendapatkan kredit khususnya Kredit Usaha Mikro. Algoritma Naive Bayes merupakan salah satu algoritma yang terdapat pada teknik klasifikasi. Bayesian classification adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Bayesian classification didasarkan pada teorema bayes yang memiliki kemampuan klasifikasi serupa dengan decission tree dan neural network. Bayesian classification terbukti memiliki akurasi dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan data yang benar. (Kusrini dan Luthfi, 2009).Hasil penelitian ini menunjukkan tingkat akurasi naïve bayes dalam memecahkan masalah pengajuan kredit sebesar 85,33 %.Kata kunci :SPK, Naive Bayesian, Klasifikasi

scholarly journals Differential Classification Method in Different Teaching Models of Accounting Courses Based on Naive Bayesian Classification Algorithm

2019 ◽  
Vol 14 (08) ◽  
pp. 38
Author(s):  
Xiuying Ou
Keyword(s):  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Self Awareness ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Accounting Courses ◽  
Generative Teaching ◽  
The Difference ◽  
Student Classification

Accounting is an important management discipline with strong theoretical foundation and practical operation. Due to the differences between individuals in the process of learning, the mastery of the subject is different. This requires teachers to implement differential teaching from the differences in student personality in the process of teaching. However, when teachers use the concept of difference teaching to teach, the classification of students' differences is mostly calculated by manual quantification such as records, tests, surveys, etc. This kind of measurement and qualitative method not only wastes manpower, but also has personal subjectivity, blindly relies on individual subjective judgment to judge students' advantages and interests, and has accuracy and scientificity. This requires research on students' differential classification methods. Therefore, this paper proposes a student classification method based on naive Bayesian algorithm. It constructs a classifier based on historical data, and then uses a well-structured and stable classifier to classify the actual pre-classification objects, and actually applies it to the teaching of accounting courses, realizing the difference in the teaching process. Provide data support for future differential teaching research. The results show that the naive Bayesian classification algorithm can be used to analyze the difference in personality and learning of students. Presupposition and generative teaching objectivesand students improve their self-awareness to better promote self-development.

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