Automated Feature Engineering for Machine Learning: Techniques and Applications
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Abstract
Feature engineering is critical in building effective machine learning models. This paper explores automated feature engineering techniques, focusing on their application across various domains. We compare manual and automated approaches, demonstrating how automation enhances model accuracy and reduces development time. The study underscores the potential of automated feature engineering in accelerating the machine learning pipeline.
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Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. https://doi.org/10.48550/arXiv.2005.14165
Chollet, F. (2017). Deep learning. MIT Press.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. https://doi.org/10.1109/CVPR.2016.90
Nadella, G. S., Gonaygunta, H., Meduri, K., & Satish, S. (2023). Adversarial Attacks on Deep Neural Network: Developing Robust Models Against Evasion Technique. Transactions on Latest Trends in Artificial Intelligence, 4(4)
Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization. International Journal of Sustainable Development in Computing Science, 5(4), 1-24.
Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. https://doi.org/10.48550/arXiv.1503.02531
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.1412.6980
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Li, Y., & Zhou, H. (2020). A survey on deep learning in medical image analysis. Medical Image Analysis, 64, 101766. https://doi.org/10.1016/j.media.2020.101766
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 4765-4774. https://doi.org/10.5555/3295222.3295230
Gonaygunta, H., Meduri, S. S., Podicheti, S., & Nadella, G. S. (2023). The Impact of Virtual Reality on Social Interaction and Relationship via Statistical Analysis. International Journal of Machine Learning for Sustainable Development, 5(2), 1-20.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
Rajpurkar, P., Irvin, J., Zhu, K., K_classifier, K., & Nguyen, D. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS), 1-11. https://doi.org/10.48550/arXiv.1711.05225