Preference learning with evolutionary Multivariate Adaptive Regression Spline model

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Abstract

This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing for human decision making. Learning models from pairwise preference data is however an NP-hard problem. Therefore, constructing models that can effectively learn such data is a challenging task. Models are usually constructed with accuracy being the most important factor. Another vitally important aspect that is usually given less attention is expressiveness, i.e. how easy it is to explain the relationship between the model input and output. Most machine learning techniques are focused either on performance or on expressiveness. This paper employ MARS models which have the advantage of being a powerful method for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed in terms of the performance, expressiveness and complexity and showed promising results in all aspects.
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This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing for human decision making. Learning models from pairwise preference data is however an NP-hard problem. Therefore, constructing models that can effectively learn such data is a challenging task. Models are usually constructed with accuracy being the most important factor. Another vitally important aspect that is usually given less attention is expressiveness, i.e. how easy it is to explain the relationship between the model input and output. Most machine learning techniques are focused either on performance or on expressiveness. This paper employ MARS models which have the advantage of being a powerful method for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed in terms of the performance, expressiveness and complexity and showed promising results in all aspects.
Original languageEnglish
Title of host publicationIEEE Congress on Evolutionary Computation (CEC)
Number of pages8
PublisherIEEE Press
Publication date2015
Pages2184 - 2191
DOI
Publication statusPublished - 2015
Publication categoryResearch
Peer-reviewedYes
Event2015 IEEE Congress on Evolutionary Computation (CEC) - Sendai, Japan
Duration: 25 May 201528 May 2015

Conference

Conference2015 IEEE Congress on Evolutionary Computation (CEC)
LandJapan
BySendai
Periode25/05/201528/05/2015

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