| Paper ID | MLSP-22.5 |
| Paper Title |
OPTIMUM FEATURE ORDERING FOR DYNAMIC INSTANCE–WISE JOINT FEATURE SELECTION AND CLASSIFICATION |
| Authors |
Yasitha Warahena Liyanage, Daphney-Stavroula Zois, University at Albany, State University of New York, United States |
| Session | MLSP-22: Sequential Learning |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-SLER] Sequential learning; sequential decision methods |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We introduce a supervised machine learning framework to perform joint feature selection and classification individually for each data instance during testing. In contrast to our prior work, we decide both the order and the number of features for each data instance. Specifically, our proposed solution dynamically selects the feature to review at each stage based on the already observed features and stops the selection process to make a prediction once it determines no classification improvement can be achieved. To gain insights, we analyze the properties of the proposed solution. Based on these properties, we propose a fast algorithm and demonstrate its effectiveness compared to the state-of-the-art using 4 publicly available datasets. |
