基于深度信念网络和线性单分类SVM的高维异常检测

doi:10.11959/j.issn.1000-0801.2018006

Abstract

Abstract:

Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.

Key words: outlier detection, high-dimensional data, deep belief network, one-class SVM

CLC Number:

TP183

Haoqi LI,Na YING,Chunsheng GUO,Jinhua WANG. High-dimensional outlier detection based on deep belief network and linear one-class SVM[J]. Telecommunications Science, 2018, 34(1): 34-42.

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算法模型	数据集				平均值
算法模型	FC	GAS	DSA	HAR	平均值
SVDD	95.45%	91.73%	82.66%	86.73%	89.14%
PCA-SVDD	96.55%	95.23%	91.48%	92.34%	93.90%
DBN-SVDD	97.92%	98.29%	96.76%	97.55%	97.63%

算法模型	数据集				平均值
算法模型	FC	GAS	DSA	HAR	平均值
SVDD	80.34%	83.54%	79.47%	78.62%	80.49%
PCA-SVDD	84.32%	91.45%	79.13%	79.85%	83.69%
DBN-SVDD	98.50%	96.52%	97.14%	98.45%	97.65%

核函数	算法模型	数据集				平均值
核函数	算法模型	FC	GAS	DSA	HAR	平均值
	AE	97.83%	97.54%	96.32%	97.25%	97.24%
RBF	DBN-SVDD	97.92%	98.29%	96.76%	97.55%	97.63%
线性	DBN-SVDD	98.50%	96.52%	97.14%	98.45%	97.65%

核函数	算法模型	数据集				平均值
核函数	算法模型	FC	GAS	DSA	HAR	平均值
线性	DBN-SVDD	0.076 8	0.096 9	0.149 5	0.241 6	0.141 2
RBF	DBN-SVDD	0.090 9	0.107 5	0.188 1	0.318 6	0.176 3
	AE	0.436 8	0.987 4	0.632 5	1.031 3	0.772 1

核函数	算法模型	数据集				平均值
核函数	算法模型	FC	GAS	DSA	HAR	平均值
线性	DBN-SVDD	0.121 3	0.183 1	0.258 1	0.562 8	0.281 3
RBF	DBN-SVDD	0.182 4	0.359 7	0.473 8	0.876 7	0.473 1
	AE	1.537 2	2.243 6	5.569 0	6.622 2	3.993 0

High-dimensional outlier detection based on deep belief network and linear one-class SVM

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