Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2018
Abstract
The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE by using three successive recurrent components to build such sequential ensembles. Given outlier scores output by an existing outlier scoring method on a feature subset, CINFO first defines a Cantelli’s inequality-based outlier thresholding function to select outlier candidates with a false positive upper bound. It then performs lasso-based sparse regression by treating the outlier scores as the target feature and the original features as predictors on the outlier candidate set to obtain a feature subset that is tailored for the outlier scoring method. Our experiments show that two different outlier scoring methods enabled by CINFO (i) perform significantly better on 11 real-life high-dimensional data sets, and (ii) have much better resilience to noisy features, compared to their bare versions and three state-of-theart competitors. The source code of CINFO is available at https://sites.google.com/site/gspangsite/sourcecode.
Keywords
Outlier Detection, Outlier Ensemble, Feature Selection, Sparse Modeling, Sequential Ensemble
Discipline
Databases and Information Systems | Data Storage Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of 32nd AAAI Conference on Artificial Intelligence 2018, New Orleans, February 2-7
Volume
32
First Page
3892
Last Page
3899
ISBN
9781577358008
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
PANG, Guansong; CAO, Longbing; CHEN, Ling; LIAN, Defu; and LIU, Huan.
Sparse modeling-based sequential ensemble learning for effective outlier detection in high-dimensional numeric data. (2018). Proceedings of 32nd AAAI Conference on Artificial Intelligence 2018, New Orleans, February 2-7. 32, 3892-3899.
Available at: https://ink.library.smu.edu.sg/sis_research/7140
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.