[en] Feature selection that selects an informative subset of variables from data not only enhances the model interpretability and performance but also alleviates the resource demands. Recently, there has been growing attention on feature selection using neural networks. However, existing methods usually suffer from high computational costs when applied to high-dimensional datasets. In this paper, inspired by evolution processes, we
propose a novel resource-efficient supervised feature selection method using sparse neural networks, named \enquote{NeuroFS}. By gradually pruning the uninformative features from the input layer of a sparse neural network trained from scratch, NeuroFS derives an informative subset of features efficiently. By performing several experiments on $11$ low and high-dimensional real-world benchmarks of different types, we demonstrate that NeuroFS achieves the highest ranking-based score among the considered state-of-the-art supervised feature selection models. The code is available on GitHub.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Atashgahi, Zahra; University of Twente
Zhang, Xuhao; University of Twente
Kichler, Neil; University of Twente
Liu, Shiwei; Eindhoven University of Technology
Yin, Lu; Eindhoven University of Technology
Pechenizkiy, Mykola; Eindhoven University of Technology
Veldhuis, Raymond; University of Twente
MOCANU, Decebal Constantin ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) ; University of Twente ; Eindhoven University of Technology
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond Veldhuis, and Mykola Pechenizkiy. A brain-inspired algorithm for training highly sparse neural networks. arXiv preprint arXiv:1903.07138, 2019.
Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond Veldhuis, and Mykola Pechenizkiy. Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Machine Learning, pp. 1–38, 2021.
Muhammed Fatih Balın, Abubakar Abid, and James Zou. Concrete autoencoders: Differentiable feature selection and reconstruction. In International Conference on Machine Learning, pp. 444–453, 2019.
Roberto Battiti. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on neural networks, 5(4):537–550, 1994.
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein. Deep rewiring: Training very sparse deep networks. In International Conference on Learning Representations, 2018.
B Chandra and Rajesh K Sharma. Exploring autoencoders for unsupervised feature selection. In 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE, 2015.
Girish Chandrashekar and Ferat Sahin. A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28, 2014.
François Chollet et al. Keras. https://keras.io, 2015.
Xiaoliang Dai, Hongxu Yin, and Niraj K Jha. Nest: A neural network synthesis tool based on a grow-and-prune paradigm. IEEE Transactions on Computers, 68(10):1487–1497, 2019.
Tim Dettmers and Luke Zettlemoyer. Sparse networks from scratch: Faster training without losing performance. arXiv preprint arXiv:1907.04840, 2019.
Guillaume Doquet and Michèle Sebag. Agnostic feature selection. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 343–358. Springer, 2019.
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen. Rigging the lottery: Making all tickets winners. In International Conference on Machine Learning, pp. 2943–2952. PMLR, 2020.
Utku Evci, Yani Ioannou, Cem Keskin, and Yann Dauphin. Gradient flow in sparse neural networks and how lottery tickets win. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp. 6577–6586, 2022.
Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. In International Conference on Learning Representations, 2018.
Trevor Gale, Erich Elsen, and Sara Hooker. The state of sparsity in deep neural networks. arXiv preprint arXiv:1902.09574, 2019.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Quanquan Gu, Zhenhui Li, and Jiawei Han. Generalized fisher score for feature selection. In 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, pp. 266–273, 2011.
Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182, 2003.
Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1):389–422, 2002.
Kai Han, Yunhe Wang, Chao Zhang, Chao Li, and Chao Xu. Autoencoder inspired unsupervised feature selection. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2941–2945. IEEE, 2018.
Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems, pp. 1135–1143, 2015.
Babak Hassibi and David G Stork. Second order derivatives for network pruning: Optimal brain surgeon. In Advances in neural information processing systems, pp. 164–171, 1993.
Xiaofei He, Deng Cai, and Partha Niyogi. Laplacian score for feature selection. In Advances in neural information processing systems, pp. 507–514, 2006.
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017.
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research, 2021.
Sara Hooker. The hardware lottery. Communications of the ACM, 64(12):58–65, 2021.
Aleks Jakulin. Machine learning based on attribute interactions. PhD thesis, University of Ljubljana, 2005.
Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, and Erich Elsen. Top-kast: Top-k always sparse training. Advances in Neural Information Processing Systems, 33:20744–20754, 2020.
S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, and Karuturi Radha Krishna Murthy. Improvements to platt’s smo algorithm for svm classifier design. Neural computation, 13(3):637–649, 2001.
Ron Kohavi and George H John. Wrappers for feature subset selection. Artificial intelligence, 97(1-2):273–324, 1997.
Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier, and René Doursat. Artificial neurogenesis: An introduction and selective review. Growing Adaptive Machines, pp. 1–60, 2014.
Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, and Tomonori Izumitani. Effective nonlinear feature selection method based on hsic lasso and with variational inference. In International Conference on Artificial Intelligence and Statistics, pp. 10407–10421. PMLR, 2022.
Yann LeCun, John S Denker, and Sara A Solla. Optimal brain damage. In Advances in neural information processing systems, pp. 598–605, 1990.
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr. Snip: Single-shot network pruning based on connection sensitivity. In International Conference on Learning Representations, 2019.
Ismael Lemhadri, Feng Ruan, Louis Abraham, and Robert Tibshirani. Lassonet: A neural network with feature sparsity. Journal of Machine Learning Research, 22(127):1–29, 2021.
Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu. Feature selection: A data perspective. ACM Computing Surveys (CSUR), 50(6):94, 2018.
Dahua Lin and Xiaoou Tang. Conditional infomax learning: An integrated framework for feature extraction and fusion. In European conference on computer vision, pp. 68–82. Springer, 2006.
Huan Liu, Rudy Setiono, et al. A probabilistic approach to feature selection-a filter solution. In ICML, volume 96, pp. 319–327. Citeseer, 1996.
Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, and Decebal Constantin Mocanu. Topological insights into sparse neural networks. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2020., 2020.
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu. Sparse training via boosting pruning plasticity with neuroregeneration. Advances in Neural Information Processing Systems (NeurIPS 2021), 2021a.
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, and Mykola Pechenizkiy. Do we actually need dense overparameterization? in-time over-parameterization in sparse training. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 6989–7000. PMLR, 18–24 Jul 2021b.
Yang Lu, Yingying Fan, Jinchi Lv, and William Stafford Noble. Deeppink: reproducible feature selection in deep neural networks. In Advances in Neural Information Processing Systems, pp. 8676–8686, 2018.
Decebal Constantin Mocanu, Elena Mocanu, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta. A topological insight into restricted boltzmann machines. Machine Learning, 104(2-3):243–270, 2016.
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature communications, 9(1):2383, 2018.
Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H Nguyen, Madeleine Gibescu, Damien Ernst, and Zita A Vale. Sparse training theory for scalable and efficient agents. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 34–38, 2021.
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. Pruning convolutional neural networks for resource efficient inference. International Conference on Learning Representations, 2017.
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz. Importance estimation for neural network pruning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
Hesham Mostafa and Xin Wang. Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 4646–4655. PMLR, 09–15 Jun 2019.
Feiping Nie, Heng Huang, Xiao Cai, and Chris Ding. Efficient and robust feature selection via joint l2, 1-norms minimization. Advances in neural information processing systems, 23, 2010.
Hanchuan Peng, Fuhui Long, and Chris Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238, 2005.
Rudy Setiono and Huan Liu. Neural-network feature selector. IEEE transactions on neural networks, 8(3): 654–662, 1997.
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, and Yuval Kluger. Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks, 152:34–43, 2022.
Dinesh Singh and Makoto Yamada. Fsnet: Feature selection network on high-dimensional biological data. arXiv preprint arXiv:2001.08322, 2020.
Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone. Dynamic sparse training for deep reinforcement learning. arXiv preprint arXiv:2106.04217, 2021.
Kenneth O Stanley and Risto Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2):99–127, 2002.
Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.
Maksymilian Wojtas and Ke Chen. Feature importance ranking for deep learning. Advances in Neural Information Processing Systems, 33:5105–5114, 2020.
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P Xing, and Masashi Sugiyama. High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1):185–207, 2014.
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, and Yuval Kluger. Feature selection using stochastic gates. In International Conference on Machine Learning, pp. 10648–10659. PMLR, 2020.
Rui Zhang, Feiping Nie, Xuelong Li, and Xian Wei. Feature selection with multi-view data: A survey. Information Fusion, 50:158–167, 2019.
