1. Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance
- Author
-
Shuang Zhou, Ralf Peeters, Xi Wu, Gijs Schoenmakers, Evgueni Smirnov, DKE Scientific staff, RS: FSE DACS, RS: FSE DACS Mathematics Centre Maastricht, and RS: FSE MaCSBio
- Subjects
0209 industrial biotechnology ,Computer science ,Cognitive Neuroscience ,Negative transfer ,Conformal map ,Feature selection ,02 engineering and technology ,Wrappers ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Feature Selection ,Ensembles ,ELDERLY-PATIENTS ,Selection (genetic algorithm) ,CONGESTIVE-HEART-FAILURE ,business.industry ,Instance transfer ,Pattern recognition ,Computer Science Applications ,Character (mathematics) ,020201 artificial intelligence & image processing ,TRIAL ,Artificial intelligence ,Conformal prediction ,business ,STANDARD MEDICAL THERAPY - 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. (C) 2019 Elsevier B.V. All rights reserved.
- Published
- 2020