A Novel Classifier Combining Supervised and Unsupervised Learning Methods

2016 ◽  
Author(s):  
Wieslaw Chmielnicki
Keyword(s):  
Unsupervised Learning ◽  
Learning Methods ◽  
Supervised And Unsupervised Learning

A Bayes-true data generator for evaluation of supervised and unsupervised learning methods

2011 ◽  
Vol 32 (11) ◽  
pp. 1523-1531 ◽  
Author(s):  
Janick V. Frasch ◽  
Aleksander Lodwich ◽  
Faisal Shafait ◽  
Thomas M. Breuel
Keyword(s):  
Unsupervised Learning ◽  
Learning Methods ◽  
Supervised And Unsupervised Learning ◽  
Data Generator

Application of Supervised and Unsupervised Learning Methods to Fault Diagnosis

1999 ◽  
Vol 32 (2) ◽  
pp. 7772-7777
Author(s):  
István Dalmi ◽  
László Kovács ◽  
István Loránt ◽  
Gábor Terstyánszky
Keyword(s):  
Fault Diagnosis ◽  
Unsupervised Learning ◽  
Learning Methods ◽  
Supervised And Unsupervised Learning

scholarly journals Supervised and Unsupervised Learning Methods for the Automation of Glomerular Scoring

2015 ◽  
Vol 29 (S1) ◽  
Author(s):  
John Bukowy ◽  
Louise Evans ◽  
Elizabeth Broadway ◽  
Alex Dayton ◽  
Allen Cowley
Keyword(s):  
Unsupervised Learning ◽  
Learning Methods ◽  
Supervised And Unsupervised Learning

scholarly journals Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1499
Author(s):  
Sungil Kim ◽  
Byungjoon Yoon ◽  
Jung-Tek Lim ◽  
Myungsun Kim
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Test Data ◽  
Active Faults ◽  
Learning Methods ◽  
Hydraulic Stimulation ◽  
Signal Noise ◽  
Supervised And Unsupervised Learning ◽  
Microseismic Data ◽  
Noise Classification

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.

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