ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

Abstract

Nowadays, many industries have applied classification algorithms to help them solve problems in their business, like finance, medicine, manufacturing industry and so on. However, in real-life scenarios, positive examples only make up a small part of all instances and our datasets suffer from high imbalance ratio which leads to poor performance of existing classification models. To solve this problem, we come up with a bagging ensemble learning framework based on an anomaly detection scoring system. We test out that our ensemble learning model can dramatically improve performance of base estimators (e.g. Decision Tree, Multilayer perceptron, KNN) and is more efficient than other existing methods under a wide range of imbalance ratio, data scale and data dimension.