Analyzing concept drift and shift from sample data
Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We propose a new data mining task, concept drift mapping —the description and analysis of instances of concept drift or shift. We argue that concept drift ma...
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Veröffentlicht in: | Data mining and knowledge discovery 2018-09, Vol.32 (5), p.1179-1199 |
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creator | Webb, Geoffrey I. Lee, Loong Kuan Goethals, Bart Petitjean, François |
description | Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We propose a new data mining task,
concept drift mapping
—the description and analysis of instances of concept drift or shift. We argue that concept drift mapping is an essential prerequisite for tackling concept drift and shift. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift and shift in marginal distributions. We present quantitative concept drift mapping techniques, along with methods for visualizing their results. We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling. |
doi_str_mv | 10.1007/s10618-018-0554-1 |
format | Article |
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concept drift mapping
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concept drift mapping
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We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling.</description><subject>Airline operations</subject><subject>Artificial Intelligence</subject><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Data mining</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Drift</subject><subject>Information Storage and Retrieval</subject><subject>Journal Track of ECML PKDD 2018</subject><subject>Machine learning</subject><subject>Mapping</subject><subject>Physics</subject><subject>Statistics for Engineering</subject><issn>1384-5810</issn><issn>1573-756X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1UEtLAzEYDKJgrf4Abwueo9-Xx272WIpaoeBFwVtIk2zdsi-T7aH-erOs4MnDMHOYGYYh5BbhHgGKh4iQo6IwQUpB8YwsUBacFjL_OE-aK0GlQrgkVzEeAEAyDgvCVp1pTt91t89s31k_jJkLdTVmpnNZ_JxUFfo2i6YdGp85M5prclGZJvqbX16S96fHt_WGbl-fX9arLbUc85EWaRGzFoFVxkJpRFGilJIpawXfGS52zAnlFDdMgFW5cqxg0pUeECwowZfkbu4dQv919HHUh_4Y0tyoGaQqnudSJRfOLhv6GIOv9BDq1oSTRtDTNXq-RsOEdI3GlGFzJiZvt_fhr_n_0A8F22P0</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Webb, Geoffrey I.</creator><creator>Lee, Loong Kuan</creator><creator>Goethals, Bart</creator><creator>Petitjean, François</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20180901</creationdate><title>Analyzing concept drift and shift from sample data</title><author>Webb, Geoffrey I. ; 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concept drift mapping
—the description and analysis of instances of concept drift or shift. We argue that concept drift mapping is an essential prerequisite for tackling concept drift and shift. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift and shift in marginal distributions. We present quantitative concept drift mapping techniques, along with methods for visualizing their results. We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10618-018-0554-1</doi><tpages>21</tpages></addata></record> |
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subjects | Airline operations Artificial Intelligence Chemistry and Earth Sciences Computer Science Data mining Data Mining and Knowledge Discovery Drift Information Storage and Retrieval Journal Track of ECML PKDD 2018 Machine learning Mapping Physics Statistics for Engineering |
title | Analyzing concept drift and shift from sample data |
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