关键词: 信用评价方法, Adaboost, 分歧度, 误分代价

Abstract: In the context of online lending, Adaboost based credit assessment approach has high disagreement level among different basic classifiers and high misclassification cost on sample set. Existing studies fail to consider the effect of basic classifiers’ disagreement and samples’ misclassification cost on the sample weights in basic classifiers, leading to the decline of the performance of credit assessment model in online lending businesses. Therefore, we propose a credit assessment approach based on an improved Adaboost model. This approach can improve the strategy of weighting in Adaboost model through considering the misclassification rates of basic classifers, the disagreement of learning results of samples among basic classifiers, and the misclassification cost of samples. Our approach can improve the validity of credit assessment results in online lending context by focusing on the samples that are difficult to classify and have high misclassification cost. The experimental results on PPDai platform show that the approach has higher accuracy and lower misclassfication cost than traditional Adaboost based credit assessment model.

Key words: credit assessment approach, adaboost, disagreement, misclassification cost