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2018 | OriginalPaper | Chapter

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

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

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Publisher: Springer International Publishing

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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.

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Literature
2.
go back to reference Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013)CrossRef Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013)CrossRef
3.
go back to reference Bose, I., Mahapatra, R.K.: Business data mining a machine learning perspective. Inf. Manag. 39(3), 211–225 (2001)CrossRef Bose, I., Mahapatra, R.K.: Business data mining a machine learning perspective. Inf. Manag. 39(3), 211–225 (2001)CrossRef
4.
go back to reference Chen, P., Wilbik, A., van Loon, S., Boer, A.-K., Kaymak, U.: Finding the optimal number of features based on mutual information. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT -2017. AISC, vol. 641, pp. 477–486. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66830-7_43CrossRef Chen, P., Wilbik, A., van Loon, S., Boer, A.-K., Kaymak, U.: Finding the optimal number of features based on mutual information. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT -2017. AISC, vol. 641, pp. 477–486. Springer, Cham (2018). https://​doi.​org/​10.​1007/​978-3-319-66830-7_​43CrossRef
5.
go back to reference Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994) Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)
7.
go back to reference Claesen, M., Simm, J., Popovic, D., Moreau, Y., De Moor, B.: Easy hyperparameter search using optunity. arXiv preprint arXiv:1412.1114 (2014) Claesen, M., Simm, J., Popovic, D., Moreau, Y., De Moor, B.: Easy hyperparameter search using optunity. arXiv preprint arXiv:​1412.​1114 (2014)
8.
go back to reference Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)MATH Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)MATH
9.
go back to reference Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002)CrossRef Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002)CrossRef
10.
go back to reference Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and Soft Computing, a Computational Approach to Learning and Machine Intelligence. Prentice-Hall Inc., Upper Saddle River (1997) Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and Soft Computing, a Computational Approach to Learning and Machine Intelligence. Prentice-Hall Inc., Upper Saddle River (1997)
11.
go back to reference Kawala, F., Douzal-Chouakria, A., Gaussier, E., Dimert, E.: Prédictions d’activité dans les réseaux sociaux en ligne. In: 4ième Conférence sur les Modèles et l’Analyse des Réseaux: Approches Mathématiques et Informatiques, p. 16 (2013) Kawala, F., Douzal-Chouakria, A., Gaussier, E., Dimert, E.: Prédictions d’activité dans les réseaux sociaux en ligne. In: 4ième Conférence sur les Modèles et l’Analyse des Réseaux: Approches Mathématiques et Informatiques, p. 16 (2013)
13.
go back to reference Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)CrossRef Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)CrossRef
17.
go back to reference Mejía-Lavalle, M., Sucar, E., Arroyo, G.: Feature selection with a perceptron neural net. In: Proceedings of the International Workshop on Feature Selection for Data Mining, pp. 131–135 (2006) Mejía-Lavalle, M., Sucar, E., Arroyo, G.: Feature selection with a perceptron neural net. In: Proceedings of the International Workshop on Feature Selection for Data Mining, pp. 131–135 (2006)
18.
go back to reference Pehro, D., Stork, D.: Pattern Classification. Wiley, New York (2001) Pehro, D., Stork, D.: Pattern Classification. Wiley, New York (2001)
19.
go back to reference Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)CrossRef Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)CrossRef
20.
go back to reference Vergara, J.R., Estévez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24(1), 175–186 (2014)CrossRef Vergara, J.R., Estévez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24(1), 175–186 (2014)CrossRef
21.
go back to reference Young, S.R., Rose, D.C., Karnowski, T.P., Lim, S.H., Patton, R.M.: Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, p. 4. ACM (2015) Young, S.R., Rose, D.C., Karnowski, T.P., Lim, S.H., Patton, R.M.: Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, p. 4. ACM (2015)
Metadata
Title
On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling
Authors
Peipei Chen
Caro Fuchs
Anna Wilbik
Tak-Ming Chan
Saskia van Loon
Arjen-Kars Boer
Xudong Lu
Volkher Scharnhorst
Uzay Kaymak
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-91479-4_13

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