53,690 research outputs found
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Department of Computer Science and EngineeringAttention mechanism is effective in both focusing the deep learning models on relevant features and
interpreting them. However, attentions may be unreliable since the networks that generate them are
often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of
input-dependent uncertainty to the attention mechanism, such that it generates attention for each
feature with varying degrees of noise based on the given input, to learn larger variance on instances it
is uncertain about. We learn this Uncertainty-aware Attention (UA) mechanism using variational
inference, and validate it on various risk prediction tasks from electronic health records on which our
model significantly outperforms existing attention models. The analysis of the learned attentions
shows that our model generates attentions that comply with clinicians' interpretation, and provide
richer interpretation via learned variance. Further evaluation of both the accuracy of the uncertainty
calibration and the prediction performance with "I don't know'' decision show that UA yields networks
with high reliability as well.ope
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often produce poor performance measures, we argue for
development of specialized techniques of data-preprocessing and classification.
In this paper, we propose a new method to simultaneously classify large
datasets and reduce the effects of missing values. It is based on a multilevel
framework of the cost-sensitive SVM and the expected maximization imputation
method for missing values, which relies on iterated regression analyses. We
compare classification results of multilevel SVM-based algorithms on public
benchmark datasets with imbalanced classes and missing values as well as real
data in health applications, and show that our multilevel SVM-based method
produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625
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