Name
Abstract | ||
|---|---|---|
We propose a novel method for outlier detection using binary decision diagrams. Leave-one-out density is proposed as a new measure for detecting outliers, which is defined as a ratio of the number of data elements inside a region to the volume of the region after a focused datum is removed. We show that leave-one-out density can be evaluated very efficiently on a set of regions around each datum in a given dataset by using binary decision diagrams. The time complexity of the proposed method is nearly linear with respect to the size of the dataset, while the outlier detection accuracy is still comparable to that of other methods. Experimental results show the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10618-016-0486-6 | Data Min. Knowl. Discov. |
Keywords | Field | DocType |
Outlier detection,Binary decision diagram,Leave-one-out-density | Local outlier factor,Data mining,Anomaly detection,Geodetic datum,Pattern recognition,Computer science,Binary decision diagram,Outlier,Artificial intelligence,Time complexity | Journal |
Volume | Issue | ISSN |
31 | 2 | 1384-5810 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuro Kutsuna | 1 | 11 | 5.00 |
| Akihiro Yamamoto | 2 | 135 | 26.84 |