Semi-automatic data annotation guided by feature space projection

PATTERN RECOGNITION

卷:109

Article

Benato, Barbara C.¹  Gomes, Jancarlo F.¹  Telea, Alexandru C.²  Falcao, Alexandre X.¹ 

[1] Univ Estadual Campinas, Inst Comp, Campinas, Brazil

[2] Univ Utrecht, Fac Sci, Dept Informat & Comp Sci, Utrecht, Netherlands

关键词: Semi-supervised learning;    Unsupervised feature learning;    Interactive data annotation;    Autoencoder-neural networks;    Data visualization;   

DOI  :  10.1016/j.patcog.2020.107612

来源: Elsevier

【 摘 要 】

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free