On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling

Peipei Chen*, Caro Fuchs, Anna Wilbik, Tak-Ming Chan, Saskia van Loon, Arjen-Kars Boer, Xudong Lu, Volkher Scharnhorst, Uzay Kaymak

*Corresponding author for this work

Research output: Contribution to conferencePaperAcademic

Abstract

Nowadays the amount of data that is collected in various settings is growing rapidly. These elaborate data records enable the training of machine learning models that can be used to extract insights and for making better informed decisions. When doing the data mining task, on one hand, feature selection is often used to reduce the dimensionality of the data. On the other hand, we need to decide the structure (parameters) of the model when building the model. However, feature selection and the parameters of the model may interact and affect the performance of the model. Therefore, it is difficult to decide the optimal parameter and the optimal feature subset without an exhaustive search of all the combination of the parameters and the feature subsets which is time-consuming. In this paper, we study how the interaction between feature selection and the parameters of a model affect the performance of the model through experiments on four data sets.
Original languageEnglish
Pages150-161
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventInternational Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems - Cadiz, Spain
Duration: 11 Jun 201815 Jun 2018

Conference

ConferenceInternational Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
Abbreviated titleIPMU 2018
Country/TerritorySpain
CityCadiz
Period11/06/1815/06/18

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