Abstract
In this work, we developed a novel approach with two main components to process raw time-series and other data forms as images. This includes a feature extraction component that returns 18 Frequency and Amplitude based Series Timed (FAST18) features for each raw input signal. The second component is the Feature to Image Transformation (FIT) algorithm which generates uniquely coded image representations of any numerical feature sets to be fed to Convolutional Neural Networks (CNNs). The study used two datasets: 1) behavioral biometrics dataset in the form of time-series signals and 2) EEG eye-tracker dataset in the form of numerical features. In earlier work, we used FAST18 to extract features from the first dataset. Different classifiers were used and Deep Neural Network (DNN) was the best. In this work, we used FIT on the same features and invoked CNN which scored 96% accuracy surpassing the best DNN results. For the second dataset, the FIT with CNN significantly outperformed DNN scoring ~90% compared to ~60%. An ablation study was performed to test noise effects on classification and the results show high tolerance to large noise. Possible extensions include time-series classification, medical signals, and physics experiments where classification is complex and critical.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003
Salman, A.S., Salman, O.S.: Spoofed/unintentional fingerprint detection using secondary biometric features. In: SAI Computing Conference, London (2020)
Salman, O., Jary, C.: Frequency and amplitude based series timed signals 18 features extraction algorithm (FAST18), pattern classification project report. SCE Carleton University, Spring 2018
Rish, I.: An empirical study of the Naive Bayes classifier, T.J. Watson Research Center, 30 Saw Mill River Road, Hawthorne, NY 10532 (2001)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand (1999)
Roesler, O.: EEG Eye State Dataset. Baden-Wuerttemberg Cooperative State University (DHBW), Stuttgart (2013)
Hatami, N., Gavet, Y., Debayle, J.: Classification of time-series images using deep convolutional neural networks. In: Proceedings of the Tenth International Conference on Machine Vision. International Society for Optics and Photonics, Vienna (2017). https://doi.org/10.1117/12.2309486
Wang, Z., Oates, T.: Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Trajectory-Based Behavior Analytics: AAAI Workshop 2015 (2015)
Cui, Z., Chen, W., Chen, Y.: Multi-scale convolutional neural networks for time series classification (2016). arXiv preprint. arXiv:1603.06995 [cs.CV]. Cornell University Library, Ithaca, NY
Azad, M., Khaled, F., Pavel, M.I.: A novel approach to classify and convert 1D signal to 2D greyscale image implementing support vector machine and imperial mode decomposition algorithm. Int. J. Adv. Res. (IJAR) 7(1), 328–335 (2019). https://doi.org/10.21474/IJAR01/8331
Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, S.A., Keogh, E.: The UCR time series archive (2018). arXiv preprint. arXiv:1810.07758 [cs.LG]. Cornell University Library, Ithaca, NY
Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: UCR Time Series Classification Archive (2015). www.cs.ucr.edu/~eamonn/time_series_data/
Hu, B., Chen, Y., Keogh, E.: Time series classification under more realistic assumptions. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Austin, Texas (2013). https://doi.org/10.1137/1.9781611972832.64
Bergen, K., Chavez, K., Ioannidis, A., Schmit, S.: Distributed Algorithms and Optimization. CME-323, Stanford Lecture Notes, Institute for Computational & Mathematical Engineering (ICME), Stanford University, CA (2015)
Acknowledgments
This work was thoroughly and critically reviewed, evaluated, and manuscript corrected by Professor Salman M Salman from Alquds University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Salman, A.S., Salman, O.S., Katz, G.E. (2020). Extending CNN Classification Capabilities Using a Novel Feature to Image Transformation (FIT) Algorithm. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-52246-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52245-2
Online ISBN: 978-3-030-52246-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)