基于信息熵差异性度量的数据流增量集成分类算法

doi:10.11959/j.issn.1000-0801.2015014

Abstract

Abstract:

The diversity between classifiers was studied and an incremental classification algorithm for data stream based on information entropy diversity measure was proposed, the method of information entropy diversity measure was integrated into the selection process of base classifiers, the information entropy diversity of base classifier which trained from training data was calculated, by means of cyclic iterative as optimization method and entropy diversity as optimization constrained goal, the numbers of base classifiers was dynamic adjusted that improved the classification accuracy and stability to reduce system costs. The experiments prove that the algorithm has less cost and strong adaptability compare with other data stream algorithm when processing data stream.

Key words: ensemble classifier, diversity measure, entropy of information, incremental ensemble, data stream

Chunhua Ju,Jiangbo Zou. An Incremental Classification Algorithm for Data Stream Based on Information Entropy Diversity Measure[J]. Telecommunications Science, 2015, 31(2): 86-96.

Figures/Tables 16

References 21

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	D_k正确分类	D_k错误分类
D_i正确分类	N¹¹	N¹⁰
D_i错误分类	N⁰¹	N⁰⁰

	Z₁	Z₂	Z₃	Z₄	Z₅	Z₆	Z₇	Z₈	Z₉	Z₁₀
D₁	0	0	0	1	1	0	1	1	1	1
D₂	1	1	1	1	0	1	0	0	0	1

	Z₁	Z₂	Z₃	Z₄	Z₅	Z₆	Z₇	Z₈	Z₉	Z₁₀
D₁	1	0	1	1	1	1	1	0	0	1
D₂	1	1	1	1	1	1	1	1	0	1

编号	分类器名称	分类器的简单描述
1	Na_?ve Bayes	朴素贝叶斯分类算法，实验中算法对连续特征属性运用核密度估计方法，而不是采用以前的样本服从一个标准正态分布
2	SMO	序列最小优化算法通过采用多项式核函数训练支持向量机
3	J48	J48决策树算法，采用Java实现C4.5算法
4	IBk	IBk是基于样本实例的一种分类算法
5	KStar	KStar分类算法通过使用最近邻和熵距离作为分类方法
6	NNge	NNge分类算法采用非嵌套泛化样本作为训练
7	PART	一种利用不完整的决策树在数据集中提取规则的算法
8	AOD	一种有效的朴素贝叶斯分类器的集成算法，它通过为每个输入属性分别建立一个超1-依赖贝叶斯分类器，然后将它们对新的样例的预测值进行平均作为集成的最终结果

数据集名称	数据量	类别数/个	属性数（标称属性/数值属性）/个
HP1	200000	10	5（0/5）
HP2	400000	5	10（0/10）
RT1	200000	8	8（4/4）
RT2	400000	4	12（7/5）
SEA1	500000	2	3（0/3）
SG1	500000	2	3（3/0）

An Incremental Classification Algorithm for Data Stream Based on Information Entropy Diversity Measure

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Figures/Tables 16

References 21

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算法		HP1	HP2	RT1	RT2	SEA1	SG1
Incremental_SEM	ACA	92.143%	91.658%	88.761%	89.874%	96.849%	95.488%
	ASM	217 MB	267 MB	344 MB	287 MB	106 MB	97 MB
Traditional_EM	ACA	90.782%	91.073%	89.896%	91.086%	96.487%	96.767%
	ASM	252 MB	318 MB	482 MB	363 MB	138 MB	115 MB

方法	HP1	HP2	RT1	RT2	SEA1	SG1
Q-statistic	1.947%	1.636%	1.976%	2.445%	0.895%	1.362%
p	1.591%	1.498%	2.164%	2.058%	0.932%	0.947%

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