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ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION
Jindong Chen; Xijin Tang
2017
Source PublicationJournal of Systems Science and Systems Engineering
ISSN1004-3756
Volume26Issue:4Pages:433
AbstractSocietal 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26346
Collection系统科学研究所
Affiliation中国科学院数学与系统科学研究院
Recommended Citation
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.
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