一种自适应的高维离群点识别方法

doi:10.16088/j.issn.1001-6600.2020.02.012

Abstract

Abstract: Aiming at the problem that the traditional distance-based outlier recognition method can not be directly and effectively applied to high-dimensional data and the recognition effect is affected by parameters, an adaptive high-dimensional outlier recognition method is proposed, which uses genetic algorithm. The optimized Gaussian Restricted Boltzmann machine nonlinearly maps high-dimensional data to low-dimensional space, and then performs outlier recognition in low-dimensional data space by adaptive outlier recognition. UCI high-dimensional data and high-dimensional data of traditional Chinese medicine are used to verify the experiment. The experimental results show that the adaptive high-dimensional outlier recognition method can adaptively and effectively identify outliers in high-dimensional data.

Key words: outlier identification, GRBM, adaptive algorithm, genetic algorithm, Chinese medicine information

CLC Number:

TP301

YE Qing, HUANG Qiang, NIE Bin, LI Huan. An Adaptive High-Dimensional Outlier Recognition Method[J].Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(2): 107-114.

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An Adaptive High-Dimensional Outlier Recognition Method

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