Abstract
In this paper we propose two methods for instance transfer based on conformal prediction. As a distinctive character, both of the methods are model independent and combine feature selection and source-instance selection to avoid negative transfer. The methods have been tested experimentally for different types of classification model on several benchmark data sets. The experimental results demonstrate that the new methods are capable of outperforming significantly standard instance transfer methods.
Original language | English |
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Pages (from-to) | 309-319 |
Number of pages | 11 |
Journal | Neurocomputing |
Volume | 397 |
DOIs | |
Publication status | Published - 15 Jul 2020 |
Keywords
- Instance transfer
- Conformal prediction
- Feature Selection
- Wrappers
- Ensembles
- STANDARD MEDICAL THERAPY
- CONGESTIVE-HEART-FAILURE
- ELDERLY-PATIENTS
- TRIAL