The Corporation Lawsuit Prediction based on Guiding Learning and Collaborative Filtering Recommendation

Abstract

It is meaningful to use data mining technology to predict the type of lawsuit which a company may receive so that enterprises can avoid lawsuit risks. So we propose a corporation lawsuit prediction algorithm based on guiding learning and collaborative filtering recommendation. Firstly, we use the adaptive synthetic sampling approach (ADASYN) to generate more synthetic data for different minority classes according to their different level of difficulty in learning, so that the training would focus on these minority classes that are difficulty to learn and reduce the learning bias introduced by the imbalance of data distribution. Secondly, for the sake of solving the problem that the insufficient samples make it difficult for the model to learn enough knowledge resulting in a large fluctuation of final scores during the training and poor model stability, we use guiding learning to integrate the basic knowledge of all types of lawsuit a company may receive in the future obtained by the multi-label classification model into the training process of TOP-1 and TOP-2 predictive models. Finally, in order to further improve the prediction accuracy, we use the collaborative filtering recommendation algorithm (CFRA) to select the most similar sample with each test sample from the training set, and the lawsuit type of the selected sample is directly used as the predicted lawsuit type of the corresponding test sample, thereby improving the total prediction accuracy. The experimental results show that the proposed algorithm can effectively predict the most probable lawsuit types of the Top2 for corporations.