Semi-automated data classification with feature weighted self organizing map

Andrew Starkey; Aliyu Usman Ahmad

doi:10.1109/FSKD.2017.8392964

Semi-automated data classification with feature weighted self organizing map

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.

Original language	English
Title of host publication	ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	136-141
Number of pages	6
ISBN (Electronic)	9781538621653
ISBN (Print)	978-1-5386-2166-0
DOIs	https://doi.org/10.1109/FSKD.2017.8392964
Publication status	Published - 25 Jun 2018
Event	13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China Duration: 29 Jul 2017 → 31 Jul 2017

Conference

Conference	13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Country/Territory	China
City	Guilin, Guangxi
Period	29/07/17 → 31/07/17

Bibliographical note

Published in: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Date of Conference: 29-31 July 2017
INSPEC Accession Number: 17862044

Keywords

Automation
Classification
Feature Weighting
Self Organizing Maps

Access to Document

10.1109/FSKD.2017.8392964Licence: Unspecified

Cite this

Semi-automated data classification with feature weighted self organizing map. / Starkey, Andrew; Ahmad, Aliyu Usman.
ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Institute of Electrical and Electronics Engineers Inc., 2018. p. 136-141.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Starkey, A & Ahmad, AU 2018, Semi-automated data classification with feature weighted self organizing map. in ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Institute of Electrical and Electronics Engineers Inc., pp. 136-141, 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017, Guilin, Guangxi, China, 29/07/17. https://doi.org/10.1109/FSKD.2017.8392964

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abstract = "This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.",

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N2 - This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.

AB - This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.

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Semi-automated data classification with feature weighted self organizing map

Abstract

Conference

Bibliographical note

Keywords

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