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Uncertainty-aware INVASE: Enhanced Breast Cancer Diagnosis Feature Selection
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2021-05-04 , DOI: arxiv-2105.02693
Jia-Xing Zhong, Hongbo Zhang

In this paper, we present an uncertainty-aware INVASE to quantify predictive confidence of healthcare problem. By introducing learnable Gaussian distributions, we lever-age their variances to measure the degree of uncertainty. Based on the vanilla INVASE, two additional modules are proposed, i.e., an uncertainty quantification module in the predictor, and a reward shaping module in the selector. We conduct extensive experiments on UCI-WDBC dataset. Notably, our method eliminates almost all predictive bias with only about 20% queries, while the uncertainty-agnostic counterpart requires nearly 100% queries. The open-source implementation with a detailed tutorial is available at https://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase%2B.ipynb.

中文翻译:

不确定性的INVASE:增强的乳腺癌诊断功能选择

在本文中,我们提出了一种不确定性感知INVASE来量化医疗保健问题的预测置信度。通过引入可学习的高斯分布,我们利用它们的方差来衡量不确定性程度。基于原始的INVASE,提出了两个附加模块,即预测器中的不确定性量化模块和选择器中的奖励整形模块。我们对UCI-WDBC数据集进行了广泛的实验。值得注意的是,我们的方法仅用大约20%的查询就消除了几乎所有的预测偏差,而与不确定性无关的方法则需要将近100%的查询。带有详细教程的开源实现可从https://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase%2B.ipynb获得。
更新日期:2021-05-07
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