Tzagarakis, G. and Panoutsos, G. (2016) Model-Based Feature Selection Based on Radial Basis Functions and Information Measures. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016 IEEE World Congress on Computational Intelligence (WCCI), 24-29 Jul 2016, Vancouver, BC, Canada. Institute of Electrical and Electronics Engineers (IEEE) , pp. 401-407. ISBN 9781509006267
Abstract
In this paper the development of a new embedded feature selection method is presented, based on a Radial-Basis-Function Neural-Fuzzy modelling structure. The proposed method is created to find the relative importance of features in a given dataset (or process in general), with special focus on manufacturing processes. The proposed approach evaluates the impact/importance of processes features by using information theoretic measures to measure the correlation between the process features and the modelling performance. Crucially, the proposed method acts during the training of the process model; hence it is an embedded method, achieving the modelling/classification task in parallel to the feature selection task. The latter is achieved by taking advantage of the information in the output layer of the Neural Fuzzy structure; in the presented case this is a TSK-type polynomial function. Two information measures are evaluated in this work, both based on information entropy: mutual information, and cross-sample entropy. The proposed methodology is tested against two popular datasets in the literature (IRIS - plant data, AirFoil - manufacturing/design data), and one more case study relevant to manufacturing - the heat treatment of steel. Results show the good and reliable performance of the developed modelling structure, on par with existing published work, as well as the good performance of the feature selection task in terms of correctly identifying important process features.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Feature selection; information entropy; information measures; Radial Basis function; Fuzzy Logic; Manufacturing |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 COMBILASER - 636902 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 May 2017 14:36 |
Last Modified: | 19 Dec 2022 13:35 |
Published Version: | https://doi.org/10.1109/FUZZ-IEEE.2016.7737715 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/FUZZ-IEEE.2016.7737715 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116414 |