Brownian distance correlation-directed search: A fast feature selection technique for alternate test |
| |
| Institution: | 1. Instituto de Microlectrónica de Sevilla, CSIC-Universidad de Sevilla, Av. Américo Vespucio s/n, 41092 Sevilla, Spain;2. CNRS, TIMA, F-38000 Grenoble, France;3. Université Grenoble Alpes, TIMA, F-38000 Grenoble, France;1. Sciences Chimiques de Rennes, UMR 6226 CNRS – INSA – Ecole Nationale Supérieure de Chimie de Rennes – Université de Rennes 1, Avenue du Général Leclerc, 35042 Rennes, France;2. Institut Jean Lamour, UMR 7198 CNRS – Université de Lorraine, Parc de Saurupt, CS 50840, 54011 Nancy, France;1. Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey;2. IMSE, CSIC and University of Sevilla, Spain;1. Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy;2. Kilby Laboratories – Silicon Valley, Texas Instruments Inc., Santa Clara, CA, USA |
| |
| Abstract: | Machine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it appears that the main bottleneck of the approach is the definition of an information-rich input space. Finding the appropriate measurements that are both cheap and meaningful is a task that has not yet been automated. In this framework, feature selection is a necessary tool to explore possible candidates. In this paper a hybrid method is proposed that lay between filtering and wrapper-based methods, trying to strike the right balance between accuracy and speed for the particular case of Alternate Test. |
| |
| Keywords: | AMS-RF test Alternate Test Machine-learning Feature selection |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
