Classification of Imbalanced Data Set in Financial Field Based on Combined Algorithm

Abstract

In view of the imbalance of data categories in financial data mining, a two-stage classification algorithm was proposed based on SVM and KNN to classify imbalanced data. In the first stage, two one-class SVM classifiers are constructed, and the samples are divided into four types: majority class (MC), minority class (mC), boundary, and outlier while the KNN algorithm is introduced to classify boundary and outlier samples in the second stage. In addition, the effectiveness of the algorithm is verified by several imbalanced data sets in the financial field. The results show that the proposed algorithm has predominant execution in financial precision marketing analysis, and compared with other algorithms, the proposed algorithm achieves better performance in G-mean and AUC and F1 indexes. This research provides an effective way for the classification of imbalanced data in the financial field.