Abstract：Imbalanced data learning is one of the research hotspots in machine learning and has received widespread attention.Synthetic Minority Oversampling TEchnique(SMOTE)is one of the mainstream methods for learning imbalanced data.In recent years,many variations of SMOTE have emerged.However,how to use sample distribution information to boost efficient oversampling performance is still a challenge.This paper proposes a supervised sample spatial distribution learning method to learn the local neighborhood information of minority samples and then it uses the local neighborhood information to constrain the downstream oversampling process.The main mechanism under the proposed method is to avoid generating potential noise samples or overlapping samples caused by the linear interpolation in the SMOTE methodology to improve the oversampling performance.Experiments on typical imbalanced data sets and seven state-of-the-art comparison methods show that our method can improve the efficiency of oversampling process by using minority neighborhood information.
2021, Vol. 42 
