{"type":"ScholarlyArticle","title":"Interpretable Minority Synthesis for Imbalanced Classification","authors":[{"name":"Yi He","slug":"yi-he","given":"Yi","family":"He","affiliation":""},{"name":"Fudong Lin","slug":"fudong-lin","given":"Fudong","family":"Lin","affiliation":""},{"name":"Xu Yuan","slug":"xu-yuan","given":"Xu","family":"Yuan","affiliation":""},{"name":"Nian-Feng Tzeng","slug":"nian-feng-tzeng","given":"Nian-Feng","family":"Tzeng","affiliation":""}],"abstract":"This  paper  proposes  a  novel  oversampling  approach that strives to balance the class priors with a considerably imbalanced data distribution of high dimensionality. The  crux  of  our  approach  lies in learning interpretable latent representations that can model the synthetic mechanism of the minority samples by using a generative adversarial network(GAN). A Bayesian regularizer is imposed to guide the GAN to extract a set of salient features that are either disentangled or intensionally entangled, with their  interplay  controlled  by  a  prescribed  structure, defined with human-in-the-loop. As such, our GAN enjoys an improved sample complexity, being able  to  synthesize  high-quality  minority  samples even if the sizes of minority classes are extremely small  during  training.   Empirical  studies  substantiate that our approach can empower simple classifiers  to  achieve  superior  imbalanced  classification performance over the state-of-the-art competitors and is robust across various imbalance settings. Code is released in github.com/fudonglin/IMSIC.","doi":"10.24963/ijcai.2021/350","url":"https://ijcai.org/proceedings/2021/350","pdf":"https://ijcai.org/proceedings/2021/0350.pdf","markdown":"https://ijcai.org/proceedings/2021/350.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/350","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":350,"pages":{"first":2542,"last":2548},"keywords":["Data Mining: Class Imbalance and Unequal Cost","Machine Learning: Learning Generative Models"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}