An Ensemble Learning-based Model for Classification of Insincere Question
Zhongyuan Han, Jiaming Gao, Huilin Sun, Ruifeng Liu, Chengzhe Huang, Leilei Kong, Haoliang Qi
Journal of E - Technology, Vol. 11, No. 2, 2020.
DOI: https://doi.org/10.6025/jet/2020/11/2/64-69
Link: http://www.dline.info/jet/fulltext/v11n2/jetv11n2_4.pdf
Abstract:
This paper describes the method for the Classification of Insincere Question(CIQ) in FIRE 2019. In this
evaluation, we use an ensemble learning method to unite multiple classification models, including logistic regression
model, support vector machine, Naive Bayes, decision tree, K-Nearest Neighbor, Random Forest. The result shows that
our classification achieves the 67.32% accuracy rate(rank top 1) on the test dataset.