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| Content Provider | ACM Digital Library |
|---|---|
| Author | Angiulli, Fabrizio Fassetti, Fabio |
| Copyright Year | 2013 |
| Abstract | This work deals with the problem of classifying uncertain data. With this aim we introduce the Uncertain Nearest Neighbor (UNN) rule, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest neighbor class of a test object is the class that maximizes the probability of providing its nearest neighbor. The evidence is that the former concept is much more powerful than the latter in the presence of uncertainty, in that it correctly models the right semantics of the nearest neighbor decision rule when applied to the uncertain scenario. An effective and efficient algorithm to perform uncertain nearest neighbor classification of a generic (un)certain test object is designed, based on properties that greatly reduce the temporal cost associated with nearest neighbor class probability computation. Experimental results are presented, showing that the UNN rule is effective and efficient in classifying uncertain data. |
| Starting Page | 1 |
| Ending Page | 35 |
| Page Count | 35 |
| File Format | |
| ISSN | 15564681 |
| e-ISSN | 1556472X |
| DOI | 10.1145/2435209.2435210 |
| Volume Number | 7 |
| Issue Number | 1 |
| Journal | ACM Transactions on Knowledge Discovery from Data (TKDD) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-03-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Classification Nearest neighbor Nearest neighbor rule Probability density functions Uncertain data |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science |
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