Advances in Instance Selection for Instance-Based Learning Algorithms
The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant...
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| Published in | Data mining and knowledge discovery Vol. 6; no. 2; pp. 153 - 172 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer Nature B.V
01.04.2002
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-5810 1573-756X |
| DOI | 10.1023/A:1014043630878 |
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| Abstract | The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner. |
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| AbstractList | The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner. |
| Author | Brighton, Henry Mellish, Chris |
| Author_xml | – sequence: 1 givenname: Henry surname: Brighton fullname: Brighton, Henry – sequence: 2 givenname: Chris surname: Mellish fullname: Mellish, Chris |
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| References_xml | – volume-title: Information filtering for lazy learning algorithms year: 1997 ident: 394822_CR4 – ident: 394822_CR7 doi: 10.1016/B978-1-55860-307-3.50009-5 – ident: 394822_CR6 doi: 10.1007/978-3-540-48247-5_31 – volume: SMC-2 start-page: 408 issue: 3 year: 1972 ident: 394822_CR30 publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1972.4309137 – volume-title: Case-Based Reasoning year: 1993 ident: 394822_CR19 – volume: C-23 start-page: 1179 year: 1974 ident: 394822_CR9 publication-title: IEEE Transactions on Computers doi: 10.1109/T-C.1974.223827 – ident: 394822_CR20 doi: 10.1016/B978-0-934613-64-4.50052-9 – start-page: 511 volume-title: Frontiers of Pattern Recognition year: 1972 ident: 394822_CR27 doi: 10.1016/B978-0-12-737140-5.50029-4 – volume-title: Nearest Neighbor (NN) norms: NN Pattern Classification Techniques year: 1991 ident: 394822_CR13 – volume: 9 start-page: 289 issue: 3 year: 1995 ident: 394822_CR18 publication-title: Applied Artificial Intelligence doi: 10.1080/08839519508945477 – volume: 21 start-page: 665 issue: 6 year: 1975 ident: 394822_CR22 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1975.1055464 – ident: 394822_CR23 doi: 10.1016/B978-1-55860-307-3.50042-3 – ident: 394822_CR8 – ident: 394822_CR14 – ident: 394822_CR2 – ident: 394822_CR31 – volume: 6 start-page: 37 issue: 1 year: 1991 ident: 394822_CR1 publication-title: Machine Learning doi: 10.1023/A:1022689900470 – ident: 394822_CR11 – ident: 394822_CR32 doi: 10.1016/B978-1-55860-247-2.50066-8 – volume: 14 start-page: 515 issue: 3 year: 1968 ident: 394822_CR16 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1968.1054155 – ident: 394822_CR26 – volume: IT-13 start-page: 21 year: 1967 ident: 394822_CR10 publication-title: IEEE. Transactions on Information Theory doi: 10.1109/TIT.1967.1053964 – ident: 394822_CR25 doi: 10.1007/978-3-540-48247-5_20 – volume: 18 start-page: 431 issue: 3 year: 1972 ident: 394822_CR15 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1972.1054809 – ident: 394822_CR29 doi: 10.3115/1603899.1603933 – ident: 394822_CR5 – ident: 394822_CR17 – volume: 10 start-page: 113 issue: 2 year: 1993 ident: 394822_CR21 publication-title: Machine Learning doi: 10.1023/A:1022614725002 – volume: 34 start-page: 11 issue: 1/3 year: 1999 ident: 394822_CR12 publication-title: Machine Learning doi: 10.1023/A:1007585615670 – volume: SMC-6 start-page: 448 issue: 6 year: 1976 ident: 394822_CR28 publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1976.4309523 – volume-title: Experiments in case-based learning year: 1996 ident: 394822_CR3 – volume: 6 start-page: 227 year: 1991 ident: 394822_CR24 publication-title: Machine Learning doi: 10.1023/A:1022661727670 |
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| Snippet | The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances... |
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| SubjectTerms | Accuracy Algorithms Classification Classification schemes Noise |
| Title | Advances in Instance Selection for Instance-Based Learning Algorithms |
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