A Genetic Algorithm Based Technique for Outlier Detection with Fast Convergence

Xiaodong Zhu, Ji Zhang, Zewen Hu, Hongzhou Li, Liang Chang, Youwen Zhu, Jerry Chun-Wei Lin, Yongrui Qin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


In this paper, we study the problem of subspace outlier detection in high dimensional data space and propose a new genetic algorithm-based tech- nique 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 exist- ing 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 op- erations and achieve an early stop of the GA that leads to more accurate approxi- mation of fitness function. This research work can contribute to the development of a more efficient search method for detecting subspace outliers. The experimen- tal results demonstrate the improved efficiency of our technique compared with the existing method.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication14th International Conference, ADMA 2018, Nanjing, China, November 16-18, 2018 Proceedings
EditorsGuojun Gan, Bohan Li, Xue Li, Shuliang Wang
Place of PublicationCham
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)9783030050900
ISBN (Print)9783030050894
Publication statusPublished - 12 Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11323 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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