Properly initialized Bayesian Network for decision making leveraging random forest

Iwakami, Yutaka; Takuma, Hironori; Iwashita, Motoi

doi:10.5430/air.v9n1p36

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Iwakami, Y. believes that due to the lack of theoretical research on Chinese data cleaning, Chinese data cleaning tools are rarely seen in the market and seldom applied to engineering projects, resulting in the current situation of single data cleaning function, poor scalability, and universality in engineering. In general, the research on Chinese data cleaning in China is still in the initial stage [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Zhang¹,

Wang²,

Lv³

2022

Advances in Multimedia

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In order to further improve the technical level of data cleaning and data mining and better avoid the defects of uncertain knowledge expression in traditional Bayesian networks, a Bayesian network algorithm based on combined data cleaning and mining technology is proposed, and a manual functional data cleaning architecture based on Hadoop is constructed. The results show that the traditional neighbor sorting algorithm with window size of 5 takes the least time to process the same amount of data. The nearest neighbor sorting algorithm with window size 7 is always the longest. The time consumption of the nonfixed window nearest neighbor sorting algorithm is similar to that of the traditional nearest neighbor sorting algorithm with a window size of 5. However, with the increase of data volume, the consumption time increases rapidly until it approaches the consumption time of the traditional sorting nearest neighbor algorithm with window size of 7. Therefore, the algorithm can improve the precision of data cleaning at the expense of cleaning speed, which proves that the artificial intelligence architecture based on combined data significantly improves the efficiency of the algorithm and can effectively analyze and process large data sets.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Zhang¹,

Wang²,

Lv³

2022

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…In this paper, the decision tree based on classification tree algorithm is used as the base evaluator of the stochastic forest model to establish the traditional stochastic forest model. Assuming that data set D contains m categories of samples, ‫݅݊݅ܩ‬ coefficient [6] of data set D is defined as:…”

Section: Establishment Of Traditional Random Forest Modelmentioning

confidence: 99%

Condition Monitoring of IGBT Devices in Internet of Things Based on Random Forest Model

Zhou,

Yao,

Wang

et al. 2024

Advances in Transdisciplinary Engineering

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Aiming at the problem of frequent aging faults of insulated gate bipolar transistor modules, this paper takes IGBT aging fault characteristic parameter data collected from the intelligent sensing layer of the Internet of Things terminal as samples. In the computing fusion cooperation layer, multi-terminal cooperation training and establishment of an optimized random forest model is applied to the IGBT aging fault diagnosis system to realize the condition monitoring of IGBT devices. Firstly, the characteristic parameters of aging fault are selected from the data samples and preprocessed to establish the aging fault diagnosis data set. Secondly, the traditional random forest model is built and optimized by parameter optimization of base evaluator, parameter optimization of model frame and bagging method. The optimization model training was completed on the basis of cross-validation. Finally, the prediction effect of the model in this paper and other models on IGBT aging fault diagnosis data set was evaluated by various evaluation indexes. Finally, the optimized model fit well, the error between the training set curve and the test set curve was 1.19%, and the prediction accuracy on the test set could reach 98.81%. The feasibility and accuracy of the optimized random forest model applied to IGBT condition monitoring system in the Internet of Things environment are verified.

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Properly initialized Bayesian Network for decision making leveraging random forest

Cited by 2 publications

References 15 publications

Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Condition Monitoring of IGBT Devices in Internet of Things Based on Random Forest Model

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