A genetic algorithm based technique for outlier detection with fast convergence
Paper
Paper/Presentation Title | A genetic algorithm based technique for outlier detection with fast convergence |
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Presentation Type | Paper |
Authors | Zhu, Xiaodong (Author), Zhang, Ji (Author), Hu, Zewen (Author), Li, Hongzhou (Author), Chang, Liang (Author), Zhu, Youwen (Author), Lin, Jerry Chun-Wei (Author) and Qin, Yongrui (Author) |
Editors | Gan, Guojun, Li, Bohan, Li, Xue and Wang, Shuliang |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 11323, pp. 95-104 |
Number of Pages | 10 |
Year | 2018 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783030050894 |
9783030050900 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-05090-0_8 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-05090-0_8 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-05090-0 |
Conference/Event | 14th International Conference on Advanced Data Mining and Applications (ADMA 2018) |
Event Details | 14th International Conference on Advanced Data Mining and Applications (ADMA 2018) Event Date 16 to end of 18 Nov 2018 Event Location Nanjing, China |
Abstract | In this paper, we study the problem of subspace outlier detection in high dimensional data space and propose a new genetic algorithm-based technique to identify outliers embedded in subspaces. The existing technique, mainly using genetic algorithm (GA) to carry out the subspace search, is generally slow due to its expensive fitness evaluation and long solution encoding scheme. In this paper, we propose a novel technique to improve the performance of the existing GA-based outlier detection method using a bit freezing approach to achieve a faster convergence. Through freezing converged bits in the solution encoding strings, this innovative approach can contribute to fast crossover and mutation operations and achieve an early stop of the GA that leads to more accurate approximation of fitness function. This research work can contribute to the development of a more efficient search method for detecting subspace outliers. The experimental results demonstrate the improved efficiency of our technique compared with the existing method. |
Keywords | outlier detection; data stream; nominal or categorical data |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Nanjing University of Information Science and Technology, China |
School of Agricultural, Computational and Environmental Sciences | |
Guilin University of Electronic Technology, China | |
Nanjing University of Aeronautics and Astronautics, China | |
Western Norway University of Applied Sciences, Norway | |
University of Huddersfield, United Kingdom | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q52w8/a-genetic-algorithm-based-technique-for-outlier-detection-with-fast-convergence
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