Bayesian Classification Method for QTL Mapping

2010 ◽  
pp. 439-456
Keyword(s):  
Qtl Mapping ◽  
Bayesian Classification ◽  
Classification Method

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 Lithofacies classification using Bayes theorem method : Case study Western Desert, Egypt

2021 ◽  
Vol 11 (1) ◽  
pp. 76-89
Author(s):  
Abdelrahman Moataz Mohamed Gomaa
Keyword(s):  
Bayesian Method ◽  
Cross Validation ◽  
Bayesian Classification ◽  
Western Desert ◽  
Classification Method ◽  
Gas Reservoir ◽  
L2 Norm ◽  
Tight Reservoirs ◽  
Lithofacies Classification

This paper shows the availability of using the Bayesian classification method to predict class membership probabilities in one of the deep tight reservoirs in Western Desert, Egypt. The workflow of our project that using the Bayesian method used the deterministic petrophysical results of three training wells to train the data and extract the classifiers. The classified data were modeled using Gaussian distribution for each lithofacies. The used wells were acquired from a deep Jurassic gas reservoir in the Western Desert of Egypt. The fitting between actual and modeled data has been reached by minimizing the L2 norm. Besides, a cross-validation process was used for validating the resulted classifiers. Finally, the Bayesian classification method can predict the GWC with an accuracy of 4 m. To avoid probability interference caused by the compacted shale more data should be added to the initial model.

scholarly journals Landslide Detection Based on Bayesian Classification Method

2019 ◽  
Vol 8 (2S3) ◽  
pp. 793-795
Keyword(s):  
Data Mining ◽  
Bayesian Classification ◽  
Classification Method ◽  
Data Sets ◽  
Mining Method ◽  
Data Mining Method ◽  
High Space

The study of landslide is a very difficult task due to high space temporal variety of involved parameters. The study of munnar city landslide has been performed by a data mining method called Bayesian classification. The dataset related to detect the landslide were soil and moisture parameters. These data sets are the basis of this work. The cumulative pattern related to the landslides depends on the data accumulated from the various sensors like geophysical sensor and moisture, soil sensor.

Landslide hazards mapping using uncertain Naïve Bayesian classification method

2015 ◽  
Vol 22 (9) ◽  
pp. 3512-3520 ◽  
Author(s):  
Yi-min Mao ◽  
Mao-sheng Zhang ◽  
Gen-long Wang ◽  
Ping-ping Sun
Keyword(s):  
Bayesian Classification ◽  
Classification Method ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Landslide Hazards

scholarly journals Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm

2021 ◽  
Vol 2083 (4) ◽  
pp. 042079
Author(s):  
Kaiying Zuo
Keyword(s):  
Mobile Communications ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Spam Filtering ◽  
White List ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
Content Based Filtering

Abstract Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.

scholarly journals Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 142 ◽  
Author(s):  
Leo Koop ◽  
Alireza Amiri-Simkooei ◽  
Karin J. van der Reijden ◽  
Sarah O’Flynn ◽  
Mirjam Snellen ◽  
...  
Keyword(s):  
Incident Angle ◽  
Particle Size Analysis ◽  
Bayesian Classification ◽  
Classification Method ◽  
Sand Wave ◽  
Size Analysis ◽  
Benthic Habitat ◽  
Full Spectrum ◽  
Trade Off ◽  
Multibeam Echosounder

High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes.

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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