Abstract
In this paper contextual neural networks with different numbers of connection groups in different layers of neurons are considered. It is verified if not-uniform patterns of numbers of groups can influence classification properties of contextual neural networks. Simulations are done in dedicated H2O machine learning environment enhanced with Generalized Backpropagation algorithm. Experiments are performed for selected UCI machine learning problems and cancer gene expression microarray data of bone marrow acute lymphatic and myeloid leukemia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mendez, K.M., Broadhurst, D., Reinke S.N., The application of artificial neural networks in metabolomics: a historical perspective. Metabolomics 15(11), 142. Springer (2019). https://doi.org/10.1007/s11306-019-1608-0
Tsai, Y.C., et al.: FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items. In: Proceedings of the 13th ACM Conference on Recom-mender Systems RecSys 2019, New York, USA, pp. 536–537. ACM (2019)
Chen, S., Zhang, S., Shang, J., Chen, B., Zheng, N.: Brain-inspired cognitive model with attention for self-driving cars. IEEE Trans. Cogn. Dev. Syst. 11(1), 13–25. IEEE (2019)
Nasser, I.M., Abu-Naser, S.S.: Lung cancer detection using artificial neural network. Int. J. of Eng. Inf. Syst. (IJEAIS) 3(3), 17–23 (2019)
Suleymanova, I., et al.: A deep convolutional neural network approach for astrocyte detection. Sci. Rep. 8(12878), 1–7 (2018)
Wang, Z.H., Horng, G.J., Hsu, T.H., Chen, C.C., Jong, G.J.: A novel facial thermal feature extraction method for non-contact healthcare system. IEEE Access 8, 86545–86553. IEEE (2020)
Qiming, Z., et al.: Artificial neural networks enabled by nanophotonics. Light: Sci. Appl. 8(1), 14. Nature Publishing Group (2019)
Guest, D., Cranmer, K., Whiteson, D.: Deep learning and its application to LHC physics. Annu. Rev. Nucl. Part. Sci. 68, 1–22 (2018)
Liu, L., Zheng, Y., Tang, D., Yuan, Y., Fan, C., Zhou, K.: Automatic skin binding for production characters with deep graph networks. ACM Trans. Graph. (SIGGRAPH) 38(4), Article 114, 12 (2019)
Gao, D., Li, X., Dong, Y., Peers, P., Xu, K., Tong, X.: Deep inverse rendering for high-resolution SVBRDF estimation from an arbitrary number of images. ACM Trans. Graph. (SIGGRAPH) 38(4), article 134, 15 (2019)
Gong, K., et al.: Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imag. 38(3), 675–685. IEEE (2019)
Munkhdalai, L., Park, K.-H., Batbaatar, E., Theera-Umpon, N., Ryu, K.H.: Deep learning-based demand forecasting for Korean postal delivery service. IEEE Access 8, 188135–188145 (2020)
Batbaatar, E., Li, M., Ryu, K.H.: Semantic-emotion neural network for emotion recognition from text. IEEE Access 7, 111866–111878. IEEE (2019)
Higgins, I., et al.: β-VAE: learning basic visual concepts with a constrained variational framework. In: International Conference Learning Representations. ICLR 2017, vol. 2, no. 5, pp. 1–22 (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations. ICLR 2018, pp. 1–26 (2018)
Huang, X., Tan, H., Lin, G., Tian, Y.: A LSTM-based bidirectional translation model for optimizing rare words and terminologies. In: 2018 IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD), China, pp. 5077–5086. IEEE (2018)
Athiwaratkun, B., Stokes, J.W.: Malware classification with LSTM and GRU language models and a character-level CNN. In: Proceedings 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), USA, 2017, pp. 2482–2486. IEEE (2017)
Amato, F., et al.: Multilayer perceptron: an intelligent model for classification and intrusion detection. In: 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan, pp. 686–691. IEEE (2017)
Dozono, H., Niina, G., Araki, S.: Convolutional self organizing map. In: 2016 IEEE International Conference on Computational Science and Computational Intelligence (CSCI), pp. 767–771. IEEE (2016)
Gościewska, K., Frejlichowski, D.: A combination of moment descriptors, fourier transform and matching measures for action recognition based on shape. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 372–386. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50417-5_28
Frejlichowski, D.: Low-level greyscale image descriptors applied for intelligent and contextual approaches. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawinski, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11431. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14799-0
Huk, M.: Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network. Int. J. App. Math. Comp. Sci. 22, 449–459 (2012)
Huk, M.: Notes on the generalized backpropagation algorithm for contextual neural networks with conditional aggregation functions. J. Intell. Fuzzy Syst. 32, 1365–1376. IOS Press (2017)
Huk, M.: Stochastic optimization of contextual neural networks with RMSprop. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds.) ACIIDS 2020. LNCS (LNAI), vol. 12034, pp. 343–352. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42058-1_29
Burnell, E.D., Wołk, K., Waliczek, K., Kern, R.: The impact of constant field of attention on properties of contextual neural networks. In: Nguyen, N.T., Trawinski, B., et al. (eds.) 12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020. LNAI, vol. 12034, pp. 364–375, Springer (2020). https://doi.org/10.1007/978-3-030-42058-1_31
Huk, M., Non-uniform initialization of inputs groupings in contextual neural networks. In: Nguyen, N., Gaol F., Hong TP., Trawiński B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. LNCS, vol. 11432, pp. 420–428. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14802-7_36
Huk, M.: Training contextual neural networks with rectifier activation functions: role and adoption of sorting methods. J. Intell. Fuzzy Syst. 37(6), 7493–7502. IOS Press (2019)
Huk, M.: Weights ordering during training of contextual neural networks with generalized error backpropagation: importance and selection of sorting algorithms. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 200–211. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_19
Szczepanik, M., et al.: Multiple classifier error probability for multi-class problems. Eksploatacja i Niezawodnosc - Maintenance and Reliability 51(3), 12–16 (2011). https://doi.org/10.17531/ein
Huk, M.: Measuring computational awareness in contextual neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, pp. 002254–002259 (2016). https://doi.org/10.1109/SMC.2016.7844574
Huk, M., Measuring the effectiveness of hidden context usage by machine learning methods under conditions of increased entropy of noise. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), Exeter, pp. 1–6 (2017). https://doi.org/10.1109/CYBConf.2017.7985787
Huk, M., Pietraszko, J.: Contextual neural-network based spectrum prediction for cognitive radio. In: 4th International Conference on Future Generation Communication Technology (FGCT 2015). IEEE Computer Society, London, UK, pp. 1–5 (2015)
Szczepanik, M., Jóźwiak, I.: Data management for fingerprint recognition algorithm based on characteristic points’ groups. In: New Trends in Databases and Information Systems. Foundations of Computing and Decision Sciences, vol. 38, no. 2, pp. 123–130, Springer (2013). https://doi.org/10.1007/978-3-642-32518-2_40
Szczepanik, M., Jóźwiak, I.: Fingerprint recognition based on minutes groups using directing attention algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS (LNAI), vol. 7268, pp. 347–354. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29350-4_42
Kwiatkowski, J., et al.: Context-sensitive text mining with fitness leveling genetic algorithm. In: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 2015, pp. 1–6. Electronic Publication (2015). https://doi.org/10.1109/CYBConf.2015.7175957. ISBN: 978-1-4799-8321-6
Huk, M.: Using context-aware environment for elderly abuse prevention. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9622, pp. 567–574. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49390-8_55
Huk, M.: Context-related data processing with artificial neural networks for higher reliability of telerehabilitation systems. In: 17th International Conference on E-health Networking, Application & Services (HealthCom). IEEE Computer Society, Boston, USA, pp. 217–221 (2015)
Privitera, C.M., Azzariti, M., Stark, L.W.: Locating regions-of-interest for the Mars Rover expedition. Int. J. Remote Sens. 21, 3327–3347. Taylor and Francis (2000)
UCI Machine Learning Repository. https://archive.ics.uci.edu/ml
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Glosser, C., Piermarocchi, C., Shanker, B.: Analysis of dense quantum dot systems using a self-consistent Maxwell-Bloch framework. In: Proceedings of 2016 IEEE International Symposium on Antennas and Propagation (USNC-URSI), Puerto Rico, pp. 1323–1324. IEEE (2016)
H2O.ai documentation. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html
Rodriguez, J.D., et al.: Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Patt. Anal. Mach. Int. 32(3), 569–575 (2010)
Bouckaert, R.R.: Estimating replicability of classifier learning experiments. In: Proceedings of the 21st International Conference on Machine Learning, Banf, Canada (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mikusova, M. et al. (2021). Towards Layer-Wise Optimization of Contextual Neural Networks with Constant Field of Aggregation. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-73280-6_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73279-0
Online ISBN: 978-3-030-73280-6
eBook Packages: Computer ScienceComputer Science (R0)