2011年7月自考真题银行会计学

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Abstract

Outlier data is the data set different data. This part of the small amount of data, but for our daily production and life of great. Therefore, the anomaly detection is widely used in network intrusion detection, finance, insurance, weather, and new drug development and other fields. Relative to the large number of normal data mining, the anomaly detection model is called data mining small. BP algorithm is a commonly used data mining algorithm. But the BP algorithm to real data outliers exist in the data mining process: the higher the dimension of the actual data, there are redundant features of the interference, and high-dimensional feature, the issue of inadequate data. Therefore, this paper analyzes a variety of BP neural network processing of data, and to get the following results. (1) BP neural network can better separation characteristics of a single simulation data; but (2) the characteristics of similar large data sets, separation is difficult to judge; (3) normal data is not sufficient or not representative, so the normal data class learning is not sufficient, leading to abnormal can not judge. To solve the above problem, this paper proposes the following improvements: (1) BP algorithm before feature reduction (map) benefit from anomaly detection features selected (2) integration of multiple neural networks, different neural network to recognize the different characteristics of each each other, the final fusion result.

Key Words：Outliers-Data，BP，Algorithms，Neural Networks

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目 录

1引言 ............................................................................................................................................................. 1

1.1背景 .................................................................................................................................................. 1 1.2传统已有异常点算法介绍 .......................................................................................................... 1

1.2.1基于统计学的异常点检测算法 ..................................................................................... 1 1.2.2基于距离的异常点检测算法 .......................................................................................... 2 1.2.3基于密度的算法 ................................................................................................................. 3 1.2.4基于偏差的异常点检测 ................................................................................................... 5 1.2.5基于聚类的异常点检测算法 .......................................................................................... 6

2基于属性特征在异常点检测中的研究 .............................................................................................. 7 3 BP神经网络介绍 ..................................................................................................................................... 9

3.1模型简介 ......................................................................................................................................... 9 3.2计算各层节点输出 ....................................................................................................................... 9 3.3 修正权值 ...................................................................................................................................... 10 4 异常检测中BP神经网络的设计 ..................................................................................................... 13

4.1可微阈值单元 .............................................................................................................................. 13 4.2单个BP网络结构设计 ............................................................................................................. 13 4.3BP神经网络学习过程的基本步骤 ......................................................................................... 14 5实验研究 .................................................................................................................................................. 17

5.1研究使用的数据库介绍 ............................................................................................................ 17 5.2训练方案一实验：把bp神经网络相似性代替距离算法相似度量 ............................ 17 5.3训练方案二实验：用单个神经网络对训练数据库整体特性进行学习 ...................... 18 5.4训练方案三实验：多神经网络各种形式训练及其决策 ................................................. 19

5.4.1实验设计思路 ................................................................................................................... 19 5.4.2实验方案及步骤 ............................................................................................................... 20 5.4.3实验分析 ............................................................................................................................ 22 5.4.4实验失败原因分析 .......................................................................................................... 23 5.5BP调参实验 ................................................................................................................................. 25

5.5.1对实验一调整隐层实验 ................................................................................................. 25 5.5.2对实验二调整隐层实验 ................................................................................................. 26 5.5.3对实验三调整隐层实验 ................................................................................................. 29 5.6数据仿真实验 .............................................................................................................................. 31

5.6.1实验思路 ............................................................................................................................ 31 5.6.2实验步骤 ............................................................................................................................ 31 5.6.3实验结果 ............................................................................................................................ 32 5.6.4结果分析 ............................................................................................................................ 33 5.7实验整体分析 .............................................................................................................................. 33 总结与展望 ................................................................................................................................................. 35 致谢 .............................................................................................................................................................. 39

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