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

doi:10.1007/978-3-319-91479-4_13

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 conference › Paper › Academic

1 Citation (Web of Science)

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 language	English
Pages	150-161
DOIs	https://doi.org/10.1007/978-3-319-91479-4_13
Publication status	Published - 2018
Externally published	Yes
Event	International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems - Cadiz, Spain Duration: 11 Jun 2018 → 15 Jun 2018

Conference

Conference	International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
Abbreviated title	IPMU 2018
Country/Territory	Spain
City	Cadiz
Period	11/06/18 → 15/06/18

Access to Document

10.1007/978-3-319-91479-4_13

Cite this

Chen, P., Fuchs, C., Wilbik, A., Chan, T-M., Loon, S. V., Boer, A-K., Lu, X., Scharnhorst, V., & Kaymak, U. (2018). On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling. 150-161. Paper presented at International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Cadiz, Spain. https://doi.org/10.1007/978-3-319-91479-4_13

@conference{04bc4398d2124bb787b688a52c7fb0e3,

title = "On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling",

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.",

author = "Peipei Chen and Caro Fuchs and Anna Wilbik and Tak-Ming Chan and Loon, {Saskia van} and Arjen-Kars Boer and Xudong Lu and Volkher Scharnhorst and Uzay Kaymak",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018 ; Conference date: 11-06-2018 Through 15-06-2018",

year = "2018",

doi = "10.1007/978-3-319-91479-4_13",

language = "English",

pages = "150--161",

}

Chen, P, Fuchs, C, Wilbik, A, Chan, T-M, Loon, SV, Boer, A-K, Lu, X, Scharnhorst, V & Kaymak, U 2018, 'On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling', Paper presented at International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Cadiz, Spain, 11/06/18 - 15/06/18 pp. 150-161. https://doi.org/10.1007/978-3-319-91479-4_13

TY - CONF

T1 - On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling

AU - Chen, Peipei

AU - Fuchs, Caro

AU - Wilbik, Anna

AU - Chan, Tak-Ming

AU - Loon, Saskia van

AU - Boer, Arjen-Kars

AU - Lu, Xudong

AU - Scharnhorst, Volkher

AU - Kaymak, Uzay

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-91479-4_13

DO - 10.1007/978-3-319-91479-4_13

M3 - Paper

SP - 150

EP - 161

T2 - International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

Y2 - 11 June 2018 through 15 June 2018

ER -