Sequential Feature Selection for Classification
In most real-world information processing problems, data is not a free resource; its acquisition is rather time-consuming and/or expensive. We investigate how these two factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to Reinforcement Learning. Our method performs a sequential feature selection that learns which features are most informative at each timestep, choosing the next feature depending on the already selected features and the internal belief of the classifier. Experiments on a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable feature cost minimization as a further property of our algorithm.
Keywordsreinforcement learning feature selection classification
Unable to display preview. Download preview PDF.
- 1.Bazzani, L., de Freitas, N., Larochelle, H., Murino, V., Ting, J.A.: Learning attentional policies for tracking and recognition in video with deep networks. In: Proceedings of the 28th International Conference on Machine Learning (2011)Google Scholar
- 4.Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, Irvine, CA (October 2011), http://archive.ics.uci.edu/ml/
- 5.Gaudel, R., Sebag, M.: Feature selection as a one-player game. In: Proceedings of the 2nd NIPS Workshop on Optimization for Machine Learning (2009)Google Scholar
- 9.Neumann, G., Pfeiffer, M., Hauser, H.: Batch reinforcement learning methods for point to point movements. Technical report, Graz University of Technology (2006)Google Scholar
- 10.Norouzi, E., Nili Ahmadabadi, M., Nadjar Araabi, B.: Attention control with reinforcement learning for face recognition under partial occlusion. Machine Vision and Applications, 1–12 (2010)Google Scholar
- 11.Perkins, S., Theiler, J.: Online feature selection using grafting. In: Proceedings of the 20th International Conference on Machine Learning (2003)Google Scholar
- 14.Schmidhuber, J., Huber, R.: Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems 2(1), 135–141 (1991)Google Scholar
- 15.Vijayakumar, S., Schaal, S.: Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space. In: Proceedings of the Seventeenth International Conference on Machine Learning (2000)Google Scholar
- 17.Wu, X., Yu, K., Wang, H., Ding, W.: Online streaming feature selection. In: Proceedings of the 27nd International Conference on Machine Learning (2010)Google Scholar