@inproceedings{70c92a2a25b3481288031c8b662b246e,
title = "CancelOut: A Layer for Feature Selection in Deep Neural Networks",
abstract = "Feature ranking (FR) and feature selection (FS) are crucial steps in data preprocessing; they can be used to avoid the curse of dimensionality problem, reduce training time, and enhance the performance of a machine learning model. In this paper, we propose a new layer for deep neural networks - CancelOut, which can be utilized for FR and FS tasks, for supervised and unsupervised learning. Empirical results show that the proposed method can find feature subsets that are superior to traditional feature analysis techniques. Furthermore, the layer is easy to use and requires adding only a few additional lines of code to a deep learning training loop. We implemented the proposed method using the PyTorch framework and published it online (The code is available at: www.github.com/unnir/CancelOut ).",
keywords = "Deep learning, Feature ranking, Feature selection, Machine learning explainability, Unsupervised feature selection",
author = "Vadim Borisov and Johannes Haug and Gjergji Kasneci",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.1007/978-3-030-30484-3_6",
language = "English",
isbn = "9783030304836",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "72--83",
editor = "Tetko, {Igor V.} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",
}