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.
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Acknowledgment
This research was partially supported by National Key Research and Development Program of China (No. 2017YFB0802300), the National Natural Science Foundation of China (No. 61602240), Guangxi Key Laboratory of Trusted Software (No. kx201615) and Capacity Building Project for Young University Staff in Guangxi Province, Department of Education, Guangxi Province (No. ky2016YB149).
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Zhu, X. et al. (2018). A Genetic Algorithm Based Technique for Outlier Detection with Fast Convergence. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_8
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