A Hybrid Reducing Error Correcting Output Code for Lithology Identification
Lithology information is critical to the adjustment of drilling control strategies, and can be identified by training a classification model from the well logging data. However, achieving accurate lithology identification is rather difficult owing to complex characteristics, such as data imbalance,...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2020-10, Vol.67 (10), p.2254-2258 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Chen, Xi Cao, Weihua Gan, Chao Hu, Wenkai Wu, Min |
| description | Lithology information is critical to the adjustment of drilling control strategies, and can be identified by training a classification model from the well logging data. However, achieving accurate lithology identification is rather difficult owing to complex characteristics, such as data imbalance, data-overlapping, and multi-classification. In this brief, a hybrid lithology identification method is developed based on the Reducing Error Correcting Output Code algorithm with the Kernel Fisher Discriminant Analysis (RECOC-KFDA). The effectiveness of the proposed method is demonstrated based on case studies with the UCI machine learning database and the real logging data. The results show that the proposed method has superior performances compared to conventional methods. |
| doi_str_mv | 10.1109/TCSII.2019.2950269 |
| format | Article |
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The results show that the proposed method has superior performances compared to conventional methods.</description><subject>Algorithms</subject><subject>Circuits and systems</subject><subject>Classification</subject><subject>Data logging</subject><subject>Discriminant analysis</subject><subject>Drilling machines</subject><subject>Encoding</subject><subject>Error correction</subject><subject>Error reduction</subject><subject>Identification methods</subject><subject>imbalanced dataset</subject><subject>Kernel</subject><subject>kernel fisher discriminant analysis</subject><subject>Lithology</subject><subject>Lithology identification</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>multi-class learning</subject><subject>reducing error correcting output codes</subject><subject>Training</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFOwzAQRC0EEqXwA3CJxDllbcd2fKwioBWVKkE5W4mzLq5KUhzn0L8noRWnWc3O7EqPkHsKM0pBP22Kj-VyxoDqGdMCmNQXZEKFyFOuNL0c50ynSmXqmtx03Q6AaeBsQt7myeJYBV8n71j31jfb5DmENiRFGwLaOBrrPh76ODg1Jm5YrXz8avft9pgsa2yid96W0bfNLbly5b7Du7NOyefL86ZYpKv167KYr1LLJY1p5oBXgBJQW2YFUiUdE5Wz1FKkoqQMMHMV8IwpUNzKUjCmFMdqEOmQT8nj6e4htD89dtHs2j40w0vDskyCBMHVkGKnlA1t1wV05hD8dxmOhoIZoZk_aGaEZs7QhtLDqeQR8b-Q57linPFfIRlntg</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Chen, Xi</creator><creator>Cao, Weihua</creator><creator>Gan, Chao</creator><creator>Hu, Wenkai</creator><creator>Wu, Min</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Xi</au><au>Cao, Weihua</au><au>Gan, Chao</au><au>Hu, Wenkai</au><au>Wu, Min</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Hybrid Reducing Error Correcting Output Code for Lithology Identification</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2020-10-01</date><risdate>2020</risdate><volume>67</volume><issue>10</issue><spage>2254</spage><epage>2258</epage><pages>2254-2258</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>Lithology information is critical to the adjustment of drilling control strategies, and can be identified by training a classification model from the well logging data. 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| subjects | Algorithms Circuits and systems Classification Data logging Discriminant analysis Drilling machines Encoding Error correction Error reduction Identification methods imbalanced dataset Kernel kernel fisher discriminant analysis Lithology Lithology identification Machine learning Measurement multi-class learning reducing error correcting output codes Training |
| title | A Hybrid Reducing Error Correcting Output Code for Lithology Identification |
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