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Scalable outlying-inlying aspects discovery via feature ranking

Publication ,  Conference
Vinh, NX; Chan, J; Bailey, J; Leckie, C; Ramamohanarao, K; Pei, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015

In outlying aspects mining, given a query object, we aim to answer the question as to what features make the query most outlying. The most recent works tackle this problem using two different strategies. (i) Feature selection approaches select the features that best distinguish the two classes: the query point vs. the rest of the data. (ii) Score-and-search approaches define an outlyingness score, then search for subspaces in which the query point exhibits the best score. In this paper, we first present an insightful theoretical result connecting the two types of approaches. Second, we present OARank – a hybrid framework that leverages the efficiency of feature selection based approaches and the effectiveness and versatility of score-and-search based methods. Our proposed approach is orders of magnitudes faster than previously proposed score-and-search based approaches while being slightly more effective, making it suitable for mining large data sets.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319180311

Publication Date

January 1, 2015

Volume

9078

Start / End Page

422 / 434

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Vinh, N. X., Chan, J., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2015). Scalable outlying-inlying aspects discovery via feature ranking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9078, pp. 422–434). https://doi.org/10.1007/978-3-319-18032-8_33
Vinh, N. X., J. Chan, J. Bailey, C. Leckie, K. Ramamohanarao, and J. Pei. “Scalable outlying-inlying aspects discovery via feature ranking.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9078:422–34, 2015. https://doi.org/10.1007/978-3-319-18032-8_33.
Vinh NX, Chan J, Bailey J, Leckie C, Ramamohanarao K, Pei J. Scalable outlying-inlying aspects discovery via feature ranking. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 422–34.
Vinh, N. X., et al. “Scalable outlying-inlying aspects discovery via feature ranking.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9078, 2015, pp. 422–34. Scopus, doi:10.1007/978-3-319-18032-8_33.
Vinh NX, Chan J, Bailey J, Leckie C, Ramamohanarao K, Pei J. Scalable outlying-inlying aspects discovery via feature ranking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 422–434.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319180311

Publication Date

January 1, 2015

Volume

9078

Start / End Page

422 / 434

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences