Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 337-342.doi: 10.11896/jsjkx.201100212

• Artificial Intelligence • Previous Articles     Next Articles

Hierarchical Learning on Unbalanced Data for Predicting Cause of Action

QU Hao1, CUI Chao-ran2, WANG Xiao-xiao2, SU Ya-xi2, HAN Xiao-hui3, YIN Yi-long1   

  1. 1 School of Software,Shandong University,Jinan 250101,China
    2 College of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China
    3 Shandong Computing Center(National Supercomputing Jinan Center),Jinan 250014,China
  • Received:2020-11-28 Revised:2021-04-14 Online:2021-12-15 Published:2021-11-26
  • About author:QU Hao,born in 1997,postgraduate.His main research interests include machine learning,natural language processing.
    YIN Yi-long,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include machine learning,data mining,and biometrics.
  • Supported by:
    National key R & D Plan Project(2018YFC0830100,2018YFC0830102).

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Abstract: The cause of action represents the nature of the legal relationships involved in the case.A scientific and rational choice of the cause of action will facilitate the correct application of laws and enable the courts to perform classification management of cases.Cause of action prediction aims to endow computers with the ability to automatically predict the cause category based on the textual case description.Due to the small number of the samples of low-frequency categories and the difficulty of learning effective features,previous methods usually filters out the samples of low-frequency category in data preprocessing.However,in the problem of predicting the cause of action,the key challenge is how to make an accurate prediction for the cases of low-frequency cause categories.To solve this problem,in this paper,we propose a novel hierarchical learning method based on unbalanced samples for predicting cause of action.Firstly,all causes are divided into the first-level and second-level causes according to their inherent hierarchical structure.Then,the tailed ones in second-level causes can be merged into a new first-level category with sufficient samples,and the hierarchical learning is applied to realize the prediction of cause of action.Finally,we refine the loss function to alleviate the problem of data imbalance.Experimental results show that the proposed method is significantly superior over the baseline methods,leading to an improvement of 4.81% in terms of accuracy.Also,we verify the benefits of introducing the hierarchical learning as well as refining the loss function for unbalanced data.

Key words: Cause of action prediction, Hierarchical learning, Loss function, Unbalanced data

CLC Number: 

  • TP391
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