Abstract
In this paper, a classification method based on an ensemble learning of deep learning and multidimensional scaling is proposed for a problem of discrimination of large and complex data. The advantage of the proposed method is improving the accuracy of results of the discrimination by removing the latent structure of data which have low explanatory power as noise, and this is done by transforming original data into a space spanned by dimensions which explain the latent structure of the data. Using numerical examples, we demonstrate the effectiveness of the proposed method.
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References
Alex, K., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: The 25th International Conference on Neural Information Processing Systems, Vol. 1, pp. 1097–1105 (2012)
Schindler, A., Lidy, T., Karner, S., Hecker, M.: Fashion and Apparel Classification using Convolutional Neural Networks, arXiv:1811.04374 (2018)
Kruskal, J.B., Wish, M.: Multidimensional Scaling. Sage Publications (1978)
Ito, K., Sato-Ilic, M.: Asymmetric dissimilarity considering the grade of attraction for interval-valued data. In: International Workshop of Fuzzy Systems & Innovational Computing, pp. 362–367 (2004)
Kunihiko, F.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp. 2278–2324 (1998)
UCI Machine Learning Repository: MHEALTH Dataset Data Set. https://archive.ics.uci.edu/ml/datasets/MHEALTH+Dataset. Last accessed 26 Aug 2020
Banos, O., Garcia, R., Holgado, J.A., Damas, M., Pomares, H., Rojas, I., Saez, A., Villalonga, C.: mHealthDroid: a novel framework for agile development of mobile health applications. In: Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014). Belfast, Northern Ireland, 2–5 Dec (2014)
Nguyen, L.T., Zeng, M., Tague, P., Zhang, J.: Recognizing new activities with limited training data. In: IEEE International Symposium on Wearable Computers (ISWC) (2015)
TensorFlow. https://www.tensorflow.org/. Last accessed 26 Aug 2020
Convolutional Neural Networks-TensorFlow. https://www.tensorflow.org/tutorials/images/cnn. Last accessed 26 Aug 2020
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747, (2017)
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Miyazawa, K., Sato-Ilic, M. (2021). A Classification Method Based on Ensemble Learning of Deep Learning and Multidimensional Scaling. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_32
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DOI: https://doi.org/10.1007/978-981-16-2765-1_32
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