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2018 | OriginalPaper | Buchkapitel

Convolutional Neural Networks in Combination with Support Vector Machines for Complex Sequential Data Classification

verfasst von : Antreas Dionysiou, Michalis Agathocleous, Chris Christodoulou, Vasilis Promponas

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. Deep Machine Learning techniques, such as Convolutional Neural Networks (CNNs), have been exclusively designed to face this class of problems. Support Vector Machines (SVMs) are a powerful technique for general classification problems, regression, and outlier detection. In this paper we present the development and implementation of an innovative by design combination of CNNs with SVMs as a solution to the Protein Secondary Structure Prediction problem, with a novel two dimensional (2D) input representation method, where Multiple Sequence Alignment profile vectors are placed one under another. This 2D input is used to train the CNNs achieving preliminary results of 80.40% per residue accuracy (Q3), which are expected to increase with the use of larger training datasets and more sophisticated ensemble methods.

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Titel: Convolutional Neural Networks in Combination with Support Vector Machines for Complex Sequential Data Classification
verfasst von: Antreas Dionysiou
Michalis Agathocleous
Chris Christodoulou
Vasilis Promponas
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2018
Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01421-6_43

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