基于频繁模式树的多来源数据选择性集成算法

doi:10.13229/j.cnki.jdxbgxb20200938

Abstract

Abstract:

In order to solve the problems of high computational difficulty and low classification accuracy when ensemble learning algorithm is used to process multi-source data sets， a multi-source data selective integration algorithm based on frequent pattern tree is proposed. By comparing the difference between data source data and truth value， the error of multi-source data is judged by using Raida criterion， and all frequent patterns of multi-source data are extracted and converted into compressed form. The frequent pattern tree structure is created， and the concept of dynamic selection is integrated. The current data is used to test the extent to which an instance belongs to a subset of wrongly classified data by adjusting the difference and accuracy of the base classifiers by weighted harmonic average， the frequent itemsets are normalized， and finally the selective ensemble of multi-source data is completed by combining the base classifiers with high precision and big difference with other base classifiers. Simulation results show that the proposed ensemble learning algorithm has better generalization performance and efficiency， and has high classification accuracy.

Key words: computer application technology, frequent pattern tree, multi-source data, selective ensemble, weight weighting, classifier

CLC Number:

TP391

Shi-min FANG. Multiple source data selective integration algorithm based on frequent pattern tree[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 885-890.

Figures/Tables 6

Fig.1

Fig.2

Table 1

Relationship between two classifiers"

分类器 $h j$	分类器 $h i$
分类器 $h j$	正确（1）	错误（0）
正确（1）	$N 11$	$N 10$
错误（0）	$N 01$	$N 00$

Table 1

Table 2

Table 3

Fig.3

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数据集	训练集	测试集	输入	输出
Boston Housing	380	120	15	1
Ozone	275	53	8	1
Ocean	352	62	12	1

数据集	算法	集成精度/%
数据集	算法	训练集	测试集
Boston Housing	本文	0.945	0.893
	文献［3］	0.871	0.865
	文献［4］	0.899	0.884
Ozone	本文	0.912	0.906
	文献［3］	0.845	0.834
	文献［4］	0.869	0.857
Ocean	本文	0.935	0.927
	文献［3］	0.886	0.874
	文献［4］	0.896	0.885

Multiple source data selective integration algorithm based on frequent pattern tree

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