UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
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DOI：
10.48550/arxiv.2210.15056
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Yanbo Xu;Alind Khare;G. Matlin;Monish Ramadoss;Rishikesan Kamaleswaran;Chao Zhang;Alexey Tumanov
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文献类型：
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作者：
Yanbo Xu;Alind Khare;G. Matlin;Monish Ramadoss;Rishikesan Kamaleswaran;Chao Zhang;Alexey Tumanov
机器学习（ML）研究重点是最大化预测任务的准确性。但是，ML模型越来越复杂，资源密集且在资源受限的环境中部署更加昂贵。这些问题对预测任务进行了加剧，并在逐渐过渡的阶段进行顺序分类，它们之间的关系是“发生的”。使用所有数据阶段，分为一系列单阶段分类器。每个单阶段分类器可以逐渐从便宜到更昂贵的二进制分类器逐步级联，这些分类器仅使用该阶段所需的必要数据方式或功能进行培训。 InfoldML是一种基于成本感和基于不确定性的动态2D预测管道，用于多个阶段分类，可实现（1）导航准确性/成本折衷空间，（2）降低通过范围，范围和范围的定义的时空成本， （3）对程序的早期预测。在临床环境中，展开的数量级成本更好，同时实时检测多阶段疾病的发展。它比表现最高的多级基线达到0.1％的准确性，同时节省了时空的推理成本和更早的（3.5小时）疾病发作预测的近20倍。我们还表明，在图像的不同级别的图像抽象的情况下，它可以预测不同级别的标签（从粗糙到细上），在该图像分类中概括了图像分类，在图像的不同级别上，将接近5倍的成本节省为0.4％的精度降低。
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to deploy in resource-constrained environments. These issues are exacerbated for prediction tasks with sequential classification on progressively transitioned stages with ''happens-before'' relation between them.We argue that it is possible to ''unfold'' a monolithic single multi-class classifier, typically trained for all stages using all data, into a series of single-stage classifiers. Each single-stage classifier can be cascaded gradually from cheaper to more expensive binary classifiers that are trained using only the necessary data modalities or features required for that stage. UnfoldML is a cost-aware and uncertainty-based dynamic 2D prediction pipeline for multi-stage classification that enables (1) navigation of the accuracy/cost tradeoff space, (2) reducing the spatio-temporal cost of inference by orders of magnitude, and (3) early prediction on proceeding stages. UnfoldML achieves orders of magnitude better cost in clinical settings, while detecting multi-stage disease development in real time. It achieves within 0.1% accuracy from the highest-performing multi-class baseline, while saving close to 20X on spatio-temporal cost of inference and earlier (3.5hrs) disease onset prediction. We also show that UnfoldML generalizes to image classification, where it can predict different level of labels (from coarse to fine) given different level of abstractions of a image, saving close to 5X cost with as little as 0.4% accuracy reduction.