基于各类支持度阈值独立挖掘的关联改进算法

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇

基于各类支持度阈值独立挖掘的关联改进算法

周忠眉1,2,李家辉1,2

（1.闽南师范大学计算机学院,福建漳州 363000；2.数据科学与智能应用福建省高等学校重点实验室，福建漳州 363000）

收稿日期:2019-04-12 修回日期:2019-06-28 出版日期:2019-11-25 发布日期:2019-11-25
基金资助:
福建省自然科学基金（2018J01545）

An associative classification algorithm based on various

class-support thresholds and independent mining rules

ZHOU Zhong-mei1,2 ,LI Jia-hui1,2

（1.School of Computer Science,Minnan Normal University,Zhangzhou 363000;

2.Key Laboratory of Data Science and Intelligence Application,Fujian Province University,Zhangzhou 363000,China）

Received:2019-04-12 Revised:2019-06-28 Online:2019-11-25 Published:2019-11-25

摘要/Abstract

摘要：

关联分类及较多的改进算法很难同时既具有较高的整体准确率又有较好的小类分类性能。针对此问题，提出了一种基于类支持度阈值独立挖掘的关联分类改进算法—ACCS。ACCS算法的主要特点是：(1)根据训练集中各类数量大小给出每个类类支持度阈值的设定方法，并基于各类的类支持度阈值独立挖掘该类的关联分类规则，尽量使小类生成更多高置信度的规则；(2) 采用类支持度对置信度相同的规则排序，提高小类规则的优先级；(3)用综合考虑置信度和提升度的新的规则度量预测未知实例。在多个数据集上的实验结果表明，相比多种关联分类改进算法，ACCS算法有更高的整体分类准确率，且在不平衡数据上也能取得较好的小类分类性能。

关键词: 关联分类, 类支持度阈值, 类支持度, 分类准确率

Abstract:

Associative classification algorithm and its existing improved algorithms cannot achieve both high overall accuracy and good minority class classification. To solve this problem, we propose an improved associative classification algorithm based on various class-support thresholds (ACCS)independent mining rules. Its main featuresare: (1) ACCS sets the support threshold of each class according to the class size in the training data, and extracts the associative classification rule of each class separately based on the class-support threshold in order to get higher confidence rules of minority classes; (2) ACCS uses the class-support threshold to rank the rules with the same confidence for increasing the priority of the minority classes; (3) ACCS combines confidence and lift degrees together to predict unknown instances. The experimental results on multiple datasets show that ACCS can achieve higher overall classification accuracy than the existing associative algorithms, and can also get good minority class classification performance in imbalanced data.

Key words: associative classification, class support threshold, class support, classification accuracy

周忠眉1,2,李家辉1,2. 基于各类支持度阈值独立挖掘的关联改进算法[J]. 计算机工程与科学.

ZHOU Zhong-mei1,2,LI Jia-hui1,2.

An associative classification algorithm based on various

class-support thresholds and independent mining rules

[J]. Computer Engineering & Science.

参考文献［5］

［1］	Sun Y,Wong A K C,Kamel M S.Classification of imbalanced data: A review［J］.International Journal of Pattern Recognition and Artificial Intelligence,2009,23(4):687-719.
［2］	Chawla N V,Japkowicz N,Kotcz A.Editorial:Special issue on learning from imbalanced data sets［J］.ACM SIGKDD Explorations Newsletter,2004,6(1):1-6.
［3］	Alwidian J,Hammo B H,Obeid N.WCBA: Weighted classification based on association rules algorithm for breast cancer disease［J］.Applied Soft Computing,2017,62:536-549.
［4］	He H,Garcia E A.Learning from imbalanced data［J］.IEEE Transactions on Knowledge & Data Engineering,2009,21(9):1263-1284.
［5］	Huang Zai-xiang, Zhou Zhong-mei, He Tian-zhong, et al.Improved
	associative classification algorithm for multiclass unbalanced datasets ［J］.Pattern Recognition and Artificial Intelligence,2015,28(10):922-929.(in Chinese)
［6］	Thabtah F. A review of associative classification mining［J］.Knowledge Engineering Review,2017,22(1):37-65.
［7］	Du Wen-liang,Zhan Zhi-jun.Building decision tree classifier on private data［C］∥Proc of IEEE International Conference on
	Data Mining Workshop on Privacy, Security, and Data Mining,2002:1-8.
［8］	Quinlan J R,Cameron-Jones R M.FOIL: A midterm report［M］∥Machine Learning: ECML-93.Berlin:Springer Berlin Heidelberg,1993:1-20.
［9］	Liu B, Hsu W,Ma Y.Integrating classification and association rule mining［C］∥Proc of International Conference on Knowledge Discovery and Data Mining,1998:80-86.
［10］	Li Wen-min, Han Jia-wei, Pei Jian. CMAR: Accurate and efficient classification based on multiple class-association rules［C］∥Proc of the 2001 IEEE International Conference on Data Mining,2001:369-376.
［11］	Yin Xiao-xin, Han Jia-wei. CPAR: Classification based on predictive association rules［C］∥
	Proc of the 2003 SIAM International Conference on Data Mining,2003:236-255.
［12］	Hadi W,Aburub F,Alhawari S.A new fast associative classification algorithm for detecting phishing websites［M］.Amsterdam:Elsevier Science Publishers, 2016.
［13］	Hadi W,Issa G,Ishtaiwi A.ACPRISM: Associative classification based on PRISM algorithm［J］.Information Sciences,2017,417:287-300.
［14］	Arunasalam B, Chawla S. CCCS:A top-down associative classifier for imbalanced class distribution［C］∥Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2006:517-522.
	附中文参考文献：
［5］	黄再祥，周忠眉，何田中，等.改进的多类不平衡数据关联分类算法［J］.模式识别与人工智能,2015,28(10):922-929.

[1]	文武, 万玉辉, 文志云, . 基于正余弦算法的文本特征选择[J]. 计算机工程与科学, 2022, 44(08): 1467-1473.
[2]	全秀祥,周忠眉，黄再祥. 一种改进的关联分类算法[J]. 计算机工程与科学, 2017, 39(10): 1966-1970.
[3]	李文轩1，李伟2，李梦凡1，刘成用1. 基于独立成分分析的优选N200和P300特征通道算法[J]. 计算机工程与科学, 2017, 39(09): 1682-1690.
[4]	刘亚，胡慧君，刘茂福. 基于混合染色体的带钢缺陷图像分类方法研究[J]. J4, 2016, 38(06): 1213-1219.
[5]	王卫平,周忠眉,郑艺峰. 基于支持度和增比率的改进关联分类算法[J]. J4, 2016, 38(02): 370-375.
[6]	黄再祥，周忠眉，何田中. 基于改进关联分类的两次学习方法[J]. J4, 2014, 36(07): 1398-1403.

基于各类支持度阈值独立挖掘的关联改进算法

An associative classification algorithm based on various

class-support thresholds and independent mining rules

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献［5］

相关文章 6

编辑推荐

Metrics

本文评价

基于各类支持度阈值独立挖掘的关联改进算法

An associative classification algorithm based on various class-support thresholds and independent mining rules

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 ［5］

相关文章 6

编辑推荐

Metrics

本文评价

An associative classification algorithm based on various

class-support thresholds and independent mining rules

参考文献［5］