Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance

Shuang Zhou*, Evgueni Smirnov, Gijs Schoenmakers, Ralf Peeters, Xi Wu

*Corresponding author for this work

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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.

Original languageEnglish
Pages (from-to)309-319
Number of pages11
JournalNeurocomputing
Volume397
DOIs
Publication statusPublished - 15 Jul 2020

Keywords

  • Instance transfer
  • Conformal prediction
  • Feature Selection
  • Wrappers
  • Ensembles
  • STANDARD MEDICAL THERAPY
  • CONGESTIVE-HEART-FAILURE
  • ELDERLY-PATIENTS
  • TRIAL

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