When less is more powerful: Shapley value attributed ablation with augmented learning for practical time series sensor data classification.

ABSTRACT: Time series sensor data classification tasks often suffer from training data scarcity issue due to the expenses associated with the expert-intervened annotation efforts. For example, Electrocardiogram (ECG) data classification for cardio-vascular disease (CVD) detection requires expensive labeling procedures with the help of cardiologists. Current state-of-the-art algorithms like deep learning models have shown outstanding performance under the general requirement of availability of large set of training examples. In this paper, we propose Shapley Attributed Ablation with Augmented Learning: ShapAAL, which demonstrates that deep learning algorithm with suitably selected subset of the seen examples or ablating the unimportant ones from the given limited training dataset can ensure consisten

SUBMITTER: Ukil A 

PROVIDER: S-EPMC9683574 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature