Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

Andrew Starkey, Aliyu Usman Ahmad, Hassan Hamdoun

Research output: Contribution to journalArticle

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

This paper investigates the application of a novel method for classification called 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 redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Original languageEnglish
Article number012006
Pages (from-to)1-7
Number of pages7
JournalIOP Conference Series: Materials Science and Engineering
Volume261
Issue number1
DOIs
Publication statusPublished - 6 Nov 2017
Event2017 International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2017) - Hawaii, United States
Duration: 30 Aug 20172 Sep 2017

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