Evolutionary Multitasking AUC Optimization

TL;DR

This paper introduces EMTAUC, an evolutionary multitasking framework that leverages small-scale, inexpensive AUC optimization tasks to improve large-scale, expensive AUC performance in imbalanced data classification.

Contribution

The paper proposes a novel multitasking approach that dynamically integrates knowledge from cheap AUC tasks to enhance large-scale AUC optimization performance.

Findings

EMTAUC outperforms single-task methods.

EMTAUC is competitive with online methods.

Dynamic data structure adjustment improves knowledge transfer.

Abstract

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at https://github.com/xiaofangxd/EMTAUC.