Fast Classification with Sequential Feature Selection in Test Phase
This addresses the challenge of efficient feature selection in classification for applications where feature acquisition is costly, though it is an incremental improvement over prior methods.
The paper tackles the problem of active feature acquisition for classification by proposing a lazy model that uses Fisher scores for sequential feature selection during testing, achieving competitive accuracy while significantly improving speed compared to existing methods.
This paper introduces a novel approach to active feature acquisition for classification, which is the task of sequentially selecting the most informative subset of features to achieve optimal prediction performance during testing while minimizing cost. The proposed approach involves a new lazy model that is significantly faster and more efficient compared to existing methods, while still producing comparable accuracy results. During the test phase, the proposed approach utilizes Fisher scores for feature ranking to identify the most important feature at each step. In the next step the training dataset is filtered based on the observed value of the selected feature and then we continue this process to reach to acceptable accuracy or limit of the budget for feature acquisition. The performance of the proposed approach was evaluated on synthetic and real datasets, including our new synthetic dataset, CUBE dataset and also real dataset Forest. The experimental results demonstrate that our approach achieves competitive accuracy results compared to existing methods, while significantly outperforming them in terms of speed. The source code of the algorithm is released at github with this link: https://github.com/alimirzaei/FCwSFS.