An improved adaboost algorithm for imbalanced data based on weighted KNN

Abstract

Imbalanced data become an obstacle in data mining nowadays, minority class sometimes are more important than majority class, just like in medical diagnosis, credit card fraud and etc. This paper focuses on the imbalanced data problem that adaboost algorithm cannot get a proper accuracy rate for minority class, and propose an improved adaboost algorithm for imbalanced data based on weighted KNN(K-Adaboost). K-Adaboost uses KNN algorithm to cut down majority class weights which is near to minority class, so that the classify can pay more attention to minority class. Besides, the paper uses a new error function and sets a threshold during classifying process in order to avoid weight distortion.