Dynamic Feature Acquisition Using Denoising Autoencoders

Abstract

In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers. In the proposed architecture, a denoising autoencoder is used to handle unknown features (i.e., features that are yet to be acquired), and the sensitivity of predictions with respect to each unknown feature is used as a context-dependent measure of informativeness. We evaluated the proposed method on eight different real-world datasets as well as one synthesized dataset and compared its performance with several other approaches in the literature. According to the results, the suggested method is capable of efficiently acquiring features at test-time in a cost- and context-aware fashion.

References in corpus (14)

• Adam: A Method for Stochastic Optimization
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Least Angle Regression
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Rejoinder to "Least angle regression" by Efron et al
• Feature selection with test cost constraint
• The Greedy Miser: Learning under Test-time Budgets

Cited by in corpus (7)

• Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
• Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
• BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis
• Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
• Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
• Adaptive Covariate Acquisition for Minimizing Total Cost of Classification
• Cost-Sensitive Feature-Value Acquisition Using Feature Relevance