ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION

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	ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION
	Jindong Chen; Xijin Tang
	2017
发表期刊	Journal of Systems Science and Systems Engineering
ISSN	1004-3756
卷号	26 期号:4 页码:433
摘要	Societal risk classification is a fundamental and complex issue for societal risk perception. To conduct societal risk classification, Tianya Forum posts are selected as the data source, and four kinds of representations: string representation, term-frequency representation, TF-IDF representation and the distributed representation of BBS posts are applied. Using edit distance or cosine similarity as distance metric, four k-Nearest Neighbor (kNN) classifiers based on different representations are developed and compared. Owing to the priority of word order and semantic extraction of the neural network model Paragraph Vector, kNN based on the distributed representation generated by Paragraph Vector (kNN-PV) shows effectiveness for societal risk classification. Furthermore, to improve the performance of societal risk classification, through different weights, kNN-PV is combined with other three kNN classifiers as an ensemble model. Through brute force grid search method, the optimal weights are assigned to different kNN classifiers. Compared with kNN-PV, the experimental results reveal that Macro-F of the ensemble method is significantly improved for societal risk classification.
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/26346
专题	系统科学研究所
作者单位	中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Jindong Chen,Xijin Tang. ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION[J]. Journal of Systems Science and Systems Engineering,2017,26(4):433.
APA	Jindong Chen,&Xijin Tang.(2017).ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION.Journal of Systems Science and Systems Engineering,26(4),433.
MLA	Jindong Chen,et al."ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION".Journal of Systems Science and Systems Engineering 26.4(2017):433.