Belief Combination of Classifiers for Incomplete Data

doi:10.1109/JAS.2022.105458

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Belief Combination of Classifiers for Incomplete Data
	Zuowei Zhang, Songtao Ye, Yiru Zhang, Weiping Ding, Hao Wang
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:4 页码:652-667
关键词	Classifier fusion,classification,evidence theory,incomplete data,missing values
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105458
英文摘要	Data with missing values, or incomplete information, brings some challenges to the development of classification, as the incompleteness may significantly affect the performance of classifiers. In this paper, we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier (BCC) method based on the evidence theory. The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness. In BCC, different attributes are regarded as independent sources, and the collection of each attribute is considered as a subset. Then, multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern. Finally, these sub-classification results with different weights (discounting factors) are used to provide supplementary information to jointly determine the final classes of query patterns. The weights consist of two aspects: global and local. The global weight calculated by an optimization function is employed to represent the reliability of each classifier, and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification. Abundant comparative experiments including seven methods on twelve datasets are executed, demonstrating the out-performance of BCC over all baseline methods in terms of accuracy, precision, recall, F1 measure, with pertinent computational costs.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47224]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Zuowei Zhang, Songtao Ye, Yiru Zhang, Weiping Ding, Hao Wang. Belief Combination of Classifiers for Incomplete Data[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(4):652-667.
APA	Zuowei Zhang, Songtao Ye, Yiru Zhang, Weiping Ding, Hao Wang.(2022).Belief Combination of Classifiers for Incomplete Data.IEEE/CAA Journal of Automatica Sinica,9(4),652-667.
MLA	Zuowei Zhang, Songtao Ye, Yiru Zhang, Weiping Ding, Hao Wang."Belief Combination of Classifiers for Incomplete Data".IEEE/CAA Journal of Automatica Sinica 9.4(2022):652-667.