Uncertainty-aware INVASE: Enhanced Breast Cancer Diagnosis Feature Selection

05/04/2021
by   Jia-Xing Zhong, et al.
0

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 20 100 available at https://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset