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.
|Title of host publication||Advanced Data Mining and Applications |
|Subtitle of host publication||14th International Conference, ADMA 2018, Nanjing, China, November 16-18, 2018 Proceedings|
|Editors||Guojun Gan, Bohan Li, Xue Li, Shuliang Wang|
|Place of Publication||Cham|
|Number of pages||10|
|Publication status||Published - 12 Jan 2019|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|