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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Guansong Pang

University of Technology Sydney


Longbing Cao

University of Technology Sydney


Ling Chen

University of Technology Sydney


Defu Lian

University of Electronic Science and Technology of China


Huan Liu

Arizona State University


DOI:

10.1609/aaai.v32i1.11692


Abstract:

The large proportion of irrelevant or noisy features in real-life 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-the-art competitors. The source code of CINFO is available at https://sites.google.com/site/gspangsite/sourcecode.

Topics: AAAI

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HOW TO CITE:

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data AAAI 2018, .

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu (2018). Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. 2018. Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. (2018) "Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu, "Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data", AAAI, p., 2018.

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. "Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. "Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Guansong Pang||Longbing Cao||Ling Chen||Defu Lian||Huan Liu. Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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