Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/414194
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dc.contributorParpinelli, R Sen_US
dc.contributorLopes, H Sen_US
dc.contributorFreitas, a aen_US
dc.date2002en_US
dc.date.accessioned2020-05-18T12:01:18Z-
dc.date.available2020-05-18T12:01:18Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/TEVC.2002.802452en_US
dc.identifier.urihttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744en_US
dc.identifier.urihttp://localhost/handle/Hannan/443326en_US
dc.identifier.urihttp://localhost/handle/Hannan/414194-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThe paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2en_US
dc.relation.haspartAL356900.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleData mining with an ant colony optimization algorithmen_US
dc.typeArticleen_US
dc.journal.titleIEEE Transactions on Evolutionary Computationen_US
Appears in Collections:2002-2008

Files in This Item:
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AL356900.pdf328.71 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributorParpinelli, R Sen_US
dc.contributorLopes, H Sen_US
dc.contributorFreitas, a aen_US
dc.date2002en_US
dc.date.accessioned2020-05-18T12:01:18Z-
dc.date.available2020-05-18T12:01:18Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/TEVC.2002.802452en_US
dc.identifier.urihttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744en_US
dc.identifier.urihttp://localhost/handle/Hannan/443326en_US
dc.identifier.urihttp://localhost/handle/Hannan/414194-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThe paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2en_US
dc.relation.haspartAL356900.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleData mining with an ant colony optimization algorithmen_US
dc.typeArticleen_US
dc.journal.titleIEEE Transactions on Evolutionary Computationen_US
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL356900.pdf328.71 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributorParpinelli, R Sen_US
dc.contributorLopes, H Sen_US
dc.contributorFreitas, a aen_US
dc.date2002en_US
dc.date.accessioned2020-05-18T12:01:18Z-
dc.date.available2020-05-18T12:01:18Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/TEVC.2002.802452en_US
dc.identifier.urihttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744en_US
dc.identifier.urihttp://localhost/handle/Hannan/443326en_US
dc.identifier.urihttp://localhost/handle/Hannan/414194-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThe paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2en_US
dc.relation.haspartAL356900.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleData mining with an ant colony optimization algorithmen_US
dc.typeArticleen_US
dc.journal.titleIEEE Transactions on Evolutionary Computationen_US
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL356900.pdf328.71 kBAdobe PDF