Fuzzy Transfer Learning: Methodology and application

Date

2014-09-18

Advisors

Journal Title

Journal ISSN

ISSN

0020-0255
1872-6291

Volume Title

Publisher

Information Sciences

Type

Article

Peer reviewed

Yes

Abstract

Producing a methodology that is able to predict output using a model is a well studied area in Computational Intelligence (CI). However, a number of real-world applications require a model but have little or no data available of the specific environment. Predominantly, standard machine learning approaches focus on a need for training data for such models to come from the same domain as the target task. Such restrictions can severely reduce the data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model such environments. It is on this particular problem that this paper is focussed.

In this paper two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By applying a TL approach through the combining of labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Fuzzy Logic, Transfer Learning

Citation

Shell, J. and Coupland, S. (2015)Fuzzy Transfer Learning: Methodology and application. Information Sciences, 293, 1 pp. 59-79

Rights

Research Institute

Institute of Artificial Intelligence (IAI)