Study on the Test Data Fault Mining Technology Based on Decision Tree

Han, Hui; Hong, Ying; Yang, Ye

doi:10.1016/j.procs.2019.06.035

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…SGD considers a set of training samples each time to find the true gradient of the objective optimization function. For each set of samples, the iterative model parameters are updated by the update rule given by formula (15):…”

Section: ) Fault Classification and Prediction Model Based On Stochastic Gradient Descentmentioning

confidence: 99%

“…Through the obtained optimal parameter set (L, R, α, N), the optimal solutions of the model parameters w of different fault types are solved by SGD iterations according to formulas ( 14) and (15), which are shown in Table 6, where a positive value indicates a positive correlation that makes the variable and the dependent variable change in the same direction; a negative value indicates a negative correlation that makes the variable and the dependent variable change in the opposite direction.…”

Section: Association Mining and Regression Trainingmentioning

confidence: 99%

“…However, most of the data mining diagnosis methods are implemented using a single algorithm model. For example, one study [15] proposed a fault diagnosis method based on decision trees for vehicle test data mining. Since the decision tree ignores the correlations of the attributes in the vehicle test data set, overfitting is prone to occur.…”

Section: Introductionmentioning

confidence: 99%

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Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Wang

et al. 2020

IEEE Access

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In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault classification and prediction methods based on big data analysis and processing have been applied in power systems. To make the classification and prediction of the fault types more accurate, this paper proposes a hybrid data mining method for power system fault classification and prediction based on clustering, association rules and stochastic gradient descent. This method uses a three-layer data mining model: The first layer uses the K-means clustering algorithm to preprocess the original fault data source, and it proposes to use self-encoding to simplify the data form. The second layer effectively eliminates the data that have little impact on the prediction results by using association rules, and the highly correlated data are mined to become the regression training data. The third layer first uses the cross-validation method to obtain the optimal parameters of each fault model, and then, it uses stochastic gradient descent for data regression training to obtain a classification and prediction model for each fault type. Finally, a verification example shows that compared with a single data mining algorithm model, the proposed method is more comparative in terms of the data mining, and the established power system fault classification and prediction model has global optimality and higher prediction accuracy, which has a certain feasibility for real-time online power system fault classification and prediction. This method reduces the disturbances from low-impact or irrelevant data by mining the fault data three times, and it uses cross-validation to optimize the multiple regression parameters of the regression model to solve the problems of low accuracy, large errors and easily falling into a local optimum, given the conduct of fault classification and prediction.

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Section: ) Fault Classification and Prediction Model Based On Stochastic Gradient Descentmentioning

confidence: 99%

Section: Association Mining and Regression Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Wang

et al. 2020

IEEE Access

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“…The next process is information summarization where the extraction of features such as user data, data content, time, events as conducted by Zhou [7], while Kenta [8] extracts geotag features on Twitter data to display the accuracy of the location of the tweet created. These features are used to avoid the possibility of data errors like Hui [9] where the C4.5 method is used as an error data analysis in the data mining process. In the same data set, C4.5 method has an accuracy of 91.59% higher than ID3, fuzzy mathematic and pair analysis set [10].…”

Section: Introductionmentioning

confidence: 99%

Twitter Data as Decision Tree Parameter for Analysis of Tourism Potential Policies

Subowo¹,

Rosyadi²,

Kusumawardhani³

2020

Proceedings of the 1st Borobudur International Symposium on Humanities, Economics and Social Sciences (BIS-HESS 2019)

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This research provides an analysis of tourism potential in Pekalongan Regency based on Twitter media so that it can provide input for related agencies to develop new potential tourism objects. Decision tree method with C4.5 is used to classify positive reviews where tourists will visit again and vice versa negative reviews with tweet data parameters related to tourism object, like location access, service satisfaction, conditions and functionality of existing facilities, and shopping experiences. The results of the review classification from the decision tree serve as input to the promotion strategy to be applied in the website media. Training data collection of 250 tweets related to the name of the tourism object in Pekalongan Regency was conducted and the experimental process was carried out in testing the model using RapidMiner 5.3. Stratified sampling with the C4.5 Decision Tree obtained the highest accuracy rate of 92% using fold = 6:4. 20 tweet sampling tests related to the Welo Asri Petungkriyono found 17 positive review tweets with the most words related to the condition and functionality of existing facilities and 3 negative reviews related to location access and service satisfaction.

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“…To satisfy these requirements addition of FSC (Fixed Series Capacitor) more precisely Thyristor Controlled Series Capacitor (TCSC) have been increased in the transmission network now a days.TCSC is one of the most important FACT device. It is used to enhance controllability, increase stability of the line, control power flow dynamically, and reduce system losses [2]. The addition of TCSC in a transmission line directly affects the performance of the distance based relay line protection process due to the rapid changes of the impedance of line.…”

Section: Introductionmentioning

confidence: 99%

Fault Classification and Fault Zone Identification in a Thyristor Controlled Series Compensator Based Transmission Line by using Decision Tree

Pradhan*,

Mohapatra,

Ghosh

et al. 2020

IJITEE

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A new method has been introduced for classification of fault and to identify zone of fault in Thyristor Controlled Series Capacitor based line by utilizing Decision Tree method. PSACD/EMTDC software is used in this paper for the simulation of TCSC. Voltage and current samples after fault are used in this method as input against predicted output vectors for zone identification of fault. Decision Tree based classification algorithm also used to classify all ten types of faults in the TCSC based line. This method is being tested on simulated data and the results indicate that this method can classify different types of faults and also identify zone of fault more accurately than any neural network systems in a TCSC based line.

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Study on the Test Data Fault Mining Technology Based on Decision Tree

Cited by 9 publications

References 4 publications

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Twitter Data as Decision Tree Parameter for Analysis of Tourism Potential Policies

Fault Classification and Fault Zone Identification in a Thyristor Controlled Series Compensator Based Transmission Line by using Decision Tree

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