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IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance - 2021

Ib-Gan: A Unified Approach For Multivariate Time Series Classification Under Class Imbalance

Research Area:  Machine Learning


Classification of large multivariate time series with strong class imbalance is an important task in real-world applications. Standard methods of class weights, oversampling, or parametric data augmentation do not always yield significant improvements for predicting minority classes of interest. Non-parametric data augmentation with Generative Adversarial Networks (GANs) offers a promising solution. We propose Imputation Balanced GAN (IB-GAN), a novel method that joins data augmentation and classification in a one-step process via an imputation-balancing approach. IB-GAN uses imputation and resampling techniques to generate higher quality samples from randomly masked vectors than from white noise, and augments classification through a class-balanced set of real and synthetic samples. Imputation hyperparameter pmiss allows for regularization of classifier variability by tuning innovations introduced via generator imputation. IB-GAN is simple to train and model-agnostic, pairing any deep learning classifier with a generator-discriminator duo and resulting in higher accuracy for under-observed classes. Empirical experiments on open-source UCR data and proprietary 90K product dataset show significant performance gains against state-of-the-art parametric and GAN baselines.


Author(s) Name:  Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson

Journal name:  Statistics

Conferrence name:  

Publisher name:  arXiv:2110.07460

DOI:  10.48550/arXiv.2110.07460

Volume Information: