ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

TL;DR

This paper introduces ASE, an ensemble learning framework that improves classification on imbalanced datasets by using anomaly detection to guide resampling, significantly enhancing performance over existing methods.

Contribution

The novel ASE framework integrates anomaly scoring with ensemble learning to effectively handle class imbalance and improve classifier performance.

Findings

ASE significantly outperforms existing methods across various imbalance ratios.

The framework is versatile and can be combined with different classifiers.

Each component of ASE is validated as reasonable and necessary.

Abstract

Nowadays, many classification algorithms have been applied to various industries to help them work out their problems met in real-life scenarios. However, in many binary classification tasks, samples in the minority class only make up a small part of all instances, which leads to the datasets we get usually suffer from high imbalance ratio. Existing models sometimes treat minority classes as noise or ignore them as outliers encountering data skewing. In order to solve this problem, we propose a bagging ensemble learning framework $ASE$ (Anomaly Scoring Based Ensemble Learning). This framework has a scoring system based on anomaly detection algorithms which can guide the resampling strategy by divided samples in the majority class into subspaces. Then specific number of instances will be under-sampled from each subspace to construct subsets by combining with the minority class. And we calculate the weights of base classifiers trained by the subsets according to the classification result of the anomaly detection model and the statistics of the subspaces. Experiments have been conducted which show that our ensemble learning model can dramatically improve the performance of base classifiers and is more efficient than other existing methods under a wide range of imbalance ratio, data scale and data dimension. $ASE$ can be combined with various classifiers and every part of our framework has been proved to be reasonable and necessary.