Inlier-Based Outlier Detection via Direct Density Ratio Estimation

  title={Inlier-Based Outlier Detection via Direct Density Ratio Estimation},
  author={Shohei Hido and Yuta Tsuboi and Hisashi Kashima and Masashi Sugiyama and Takafumi Kanamori},
  journal={2008 Eighth IEEE International Conference on Data Mining},
We propose a new statistical approach to the problem of inlier-based outlier detection, i.e.,finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score; we estimate the ratio directly in a semi-parametric fashion without going through density estimation. Thus our approach is expected to have better performance in high-dimensional problems. Furthermore, the applied algorithm for… CONTINUE READING
Highly Cited
This paper has 96 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
46 Extracted Citations
23 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 46 extracted citations

97 Citations

Citations per Year
Semantic Scholar estimates that this publication has 97 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 23 references

Similar Papers

Loading similar papers…