Abstract
With the explosion of interest in machine learning (ML)-based classification algorithms applied to streaming data sources, there is a decided benefit to the rapid decisions that a neural network (NN) can provide. However, there is also a desire to integrate successive real-time decisions on streaming data, which likely have temporal correlations, into an overall higher confidence result. This paper offers a predictive approach to aggregating the results of discrete classification outputs based upon that duration of temporal correlation, with specific examples for both an image processing (facial mask recognition) application and two radio frequency (RF) ML applications (specific emitter identification and automatic modulation classification). The decision aggregation technique employs a multinomial distribution representation of conditional decision probabilities, drawn from the confusion matrices of the classification problems, to show that an ML classifier possessing even a marginal ability to improve upon random guessing has the potential to drastically improve overall decision accuracy when operating on large continuous streams of data, boosting confidence in the resulting decision.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We assume that the sampling process, such as capturing an image or measuring a signal, do not induce a temporal correlation to the decisions that is different from the actual object/signal of interest, such as might occur with plenoptic cameras or overlapping time windows as single frames.
References
Abbas, Q., Ibrahim, M., Jaffar, M.: A comprehensive review of recent advances on deep vision systems. Artif. Intell. Rev. 52, 39–76 (2019). https://doi.org/10.1007/s10462-018-9633-3
Sharma, A.R., Kaushik, P.: Literature survey of statistical, deep and reinforcement learning in natural language processing. In: 2017 International Conference on Computing, Communication and Automation (ICCCA 2017), pp. 350–354 (2017). https://doi.org/10.1109/CCAA.2017.8229841
Bkassiny, M., Li, Y., Jayaweera, S.K.: A Survey on machine-learning techniques in cognitive radios. IEEE Commun. Surv. Tutor. 15(3), 1136–1159 (2013). https://doi.org/10.1109/SURV.2012.100412.00017
Wong, L., et al.: An RFML ecosystem: considerations for the application of machine learning to spectrum situational awareness applications. arXiv https://arxiv.org/abs/2010.00432
Fatima, S., Kumar, A., Pratap, A., Raoof, S.: Object recognition and detection in remote sensing images: a comparative study. In: 2020 International Conference on Artificial Intelligence and Signal Processing (AISP), pp. 1–5. Amaravati, India (2020) https://doi.org/10.1109/AISP48273.2020.9073614
Tu, Z., Xie, W., Dauwels, J., Li, B., Yuan, J.: Semantic cues enhanced multimodality multistream CNN for action recognition. IEEE Trans. Circ. Syst. Video Technol. 29(5), 1423–1437 (2019). https://doi.org/10.1109/TCSVT.2018.2830102
Barabas, J., Zalman, R., Kochlan, M.: Automated evaluation of COVID-19 risk factors coupled with real-time, indoor, personal localization data for potential disease identification, prevention and smart quarantining. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 645–648. Milan, Italy (2020). https://doi.org/10.1109/TSP49548.2020.9163461
Nivetha, S.: A Survey on speech feature extraction and classification techniques. In: 2020 International Conference on Inventive Computation Technologies (ICICT), pp. 48–53. Coimbatore, India (2020). https://doi.org/10.1109/ICICT48043.2020.9112582
Ghorpade, T., Ragha, L.: Featured based sentiment classification for hotel reviews using NLP and Bayesian classification. In: 2012 International Conference on Communication, Information & Computing Technology (ICCICT), pp. 1–5. Mumbai, India (2012). https://doi.org/10.1109/ICCICT.2012.6398136
Romero, P., Dighe, K.: Fast and unsupervised classification of radio frequency data sets utilizing machine learning algorithms. Data Analyt. 2015, 146–152 (2015)
Hauser, S.C., Headley, W.C., Michaels, A.J.: Signal detection effects on deep neural networks utilizing raw IQ for modulation classification. In: Military Communications Conference (MILCOM 2017), pp. 121–127. IEEE, MD, Baltimore (2017)
Merchant, K., Revay, S., Stantchev, G., Nousain, B.: Deep learning for rf device fingerprinting in cognitive communication networks. IEEE J. Select. Top. Sig. Process. 12(1), 160–167 (2018)
Kim, K., Spooner, C.M., Akbar, I., Reed, J.H.: Specific emitter identification for cognitive radio with application to IEEE 802.11. In: IEEE GLOBECOM 2008–2008 IEEE Global Telecommunications Conference, pp. 1–5. New Orleans (2008)
Cain, I., Clark, J., Pauls, E., Ausdenmoore, B., Clouse, R., Josue, T.: Convolutional neural networks for radar emitter classification. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 79–83. Las Vegas (2018)
Selim, A., Paisana, F., Arokkiam, J., Zhang, Y., Doyle, L., DaSilva, L.: Spectrum monitoring for radar bands using deep convolutional neural networks. In: GLOBECOM 2017–2017 IEEE Global Communications Conference, pp. 1–6. Singapore (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
Wang, Z., et al.: Masked face recognition dataset and application. arXiv (2020). https://arxiv.org/abs/2003.09093
Billings, R.: On efficient computer vision applications for neural networks. Masters Thesis, Virginia Tech (2021)
Emam, A., Shalaby, M., Aboelazm, M., Bakr, H., Mansour, H.: A Comparative study between CNN, LSTM, and CLDNN models in the context of radio modulation classification. In: 2020 12th International Conference on Electrical Engineering (ICEENG), pp. 190–195. Cairo, Egypt (2020). https://doi.org/10.1109/ICEENG45378.2020.9171706
Wong, L.J., Headley, W.C., Michaels, A.J.: Specific emitter identification using convolutional neural network-based IQ imbalance estimators. IEEE Access 7, 33544–33555 (2019). https://doi.org/10.1109/ACCESS.2019.2903444
Zhuo, F., Huang, Y., Chen, J.: Radio frequency fingerprint extraction of radio emitter based on I/Q imbalance. Procedia Comput. Sci. 107, 472–477 (2017)
Zeiler, M.D., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2013)
Springenberg, J.T., Riedmiller, M.: Improving deep neural networks with probabilistic maxout units. CoRR. https://arxiv.org/abs/1312.6116
Gu, J., et al.: Recent advances in convolutional neural networks. Patt. Recogn. 77, 354–377 (2018). ISSN: 0031-3203. https://doi.org/10.1016/j.patcog.2017.10.013
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Bakir, S.: A subset selection procedure for multinomial distributions. J. Appl. Stat. 40(7), 1608–1618 (2013)
Wang, Z., et al.: Masked face recognition dataset and application. arXiv preprint arXiv:2003.09093 (2020)
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
Michaels, A.J., Wong, L.J. (2021). Multinomial-Based Decision Synthesis of ML Classification Outputs. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2021. Lecture Notes in Computer Science(), vol 12898. Springer, Cham. https://doi.org/10.1007/978-3-030-85529-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-85529-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85528-4
Online ISBN: 978-3-030-85529-1
eBook Packages: Computer ScienceComputer Science (R0)