Cost-sensitive Convolutional Neural Network Based Hybrid Method for Imbalanced Data Classification

Abstract

The imbalance classification is a common problem in the field of data mining.In general,the skewed distribution of data makes the classification effect of the classifier unsatisfactory.As an efficient data mining tool,convolutional neural network is widely used in classification tasks.However,if the training process is adversely affected by data imbalance,it will cause the classification accuracy of minority classes to decrease.Aiming at the classification problem of two-class unbalanced data,this paper proposes a hybrid method for unbalanced classification problems based on cost-sensitive convolutional neural networks.The proposed method first combines the density peak clustering algorithm with SMOTE,and preprocesses the data through oversampling to reduce the imbalance of the original data set.Then the cost sensitive is used to give different weights to different categories in the unbalanced data.Additionally,the Euclidean distance between the predicted value and the label value is considered.The proposed method assigns different cost losses to the majority class and the minority class in the unbalanced data to construct cost sensitivity convolutional neural network model to improve the recognition rate of convolutional neural network for minority classes.Six different datasets are used to verify the effectiveness of the proposed method.The experimental results show that the proposed method is able to improve the classification performance of the convolutional neural network model on unbalanced data.

Keywords

• imbalance classification|convolutional neural network|oversampling|data preprocessing|cost-sensitive loss function