ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION

• Published in: Journal of systems science and systems engineering Vol. 26; no. 4; pp. 433 - 447
• Main Authors: Chen, Jindong, Tang, Xijin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-08-2017; Springer Nature B.V; Academy of Mathematics and Systems Science, Chinese Academy of Sciences,Beijing, 100190, P.R.China; China Aerospace Academy of Systems Science and Engineering, Beijing, 100048, P.R.China%Academy of Mathematics and Systems Science, Chinese Academy of Sciences,Beijing, 100190, P.R.China
• Subjects: Classification; Classifiers; Complexity; Economic Theory/Quantitative Economics/Mathematical Methods; Engineering; Forum; KNN算法; Neural networks; Operations Research/Decision Theory; Representations; Risk perception; 余弦相似度; 分类器集成; 字符串表; 社会风险; 神经网络模型; 风险分类; Paragraph Vector; k-Nearest Neighbor; ensemble; Tianya Forum; Societal risk classification
• ISSN: 1004-3756; 1861-9576
• DOI: 10.1007/s11518-017-5346-4

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.