mkachuee / DynamicFeatureAcquisition

Source code for: M. Kachuee et al., Dynamic Feature Acquisition Using Denoising Autoencoders, IEEE TNNLS, 2018.

Dynamic Feature Acquisition Using Denoising Autoencoders

This repository provides the source code for the paper:

M. Kachuee, S. Darabi, B. Moatamed, M. Sarrafzadeh, Dynamic Feature Acquisition Using Denoising Autoencoders, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018. Paper

Note: The provided source code is only tested on TensorFlow 1.0 and may not be compatible with more recent versions.

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Citation Request

Please use the following citation format:

@article{kachuee2018dynamic,
  title={Dynamic feature acquisition using denoising autoencoders},
  author={Kachuee, Mohammad and Darabi, Sajad and Moatamed, Babak and Sarrafzadeh, Majid},
  journal={IEEE transactions on neural networks and learning systems},
  year={2018},
  publisher={IEEE}
}

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Related Work

• M. Kachuee, O. Goldstein, K. Kärkkäinen, S. Darabi, M. Sarrafzadeh, Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams, International Conference on Learning Representations (ICLR), 2019. Paper, Code/Dataset

• M. Kachuee, K. Karkkainen, O. Goldstein, D. Zamanzadeh, M. Sarrafzadeh, Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data, arXiv:1902.07102, 2019. Paper, Dataset