Machine Learning: A Review

Authors

  • Isonkobong Christopher Udousoro Department of Information Technology, School of Computing and Information Technology, Federal University of Technology, Owerri

DOI:

https://doi.org/10.30564/ssid.v2i2.1931

Abstract

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on.The paper aims at reviewing machine learning techniques and algorithms.The research methodology is based on qualitative analysis where various literatures is being reviewed based on machine learning.

 

Keywords:

Machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; Semi-supervised learning; Multitask learning; Ensemble learning; Neural Network; Instance-Based

References

[1] Alsheikh, M. A., Lin, S., Niyato, D., Tan, H. P. Machine learning in wireless sensor networks:Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 2014,16(4): 1996-2018.

[2] Arroyo, J., Guijarro, M., Pajares, G. An instance-based learning approach for thresholding in crop images under different outdoor conditions. Computers and Electronics in Agriculture, 2016, 127:669-679.

[3] Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas,H., He, Y. L. Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 2017, 378: 484-497.

[4] Aubourg, É., Bartlett, J., Boucaud, A., Ganga, K.,Giraud-Héraud, Y., Le Jeune, M., LAL, J. É. C. Prospective IN2P3 Survey Synergies with Machine Learning GT05+ GT09, 2019.

[5] Bakoev, S., Getmantseva, L., Kolosova, M., Kostyunina, O., Chartier, D., Tatarinova, T. V. PigLeg: Prediction of Swine Phenotype Using Machine Learning, 2019.

[6] Balzer, L. B., Havlir, D. V., Kamya, M. R., Chamie,G., Charlebois, E. D., Clark, T. D., Kabami, J. Machine learning to identify persons at high-risk of HIV acquisition in rural Kenya and Uganda.Clinical Infectious Diseases, 2019.

[7] Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P.,Wiebe, N., Lloyd, S. Quantum machine learning. Nature, 2017, 549(7671): 195-202.

[8] Bojanowski, P., Joulin, A. Unsupervised learning by predicting noise. In Proceedings of the 34th International Conference on Machine Learning. JMLR. Org,2017, 70: 517-526.

[9] Bostanabad, R., Bui, A. T., Xie, W., Apley, D. W.,Chen, W. Stochastic microstructure characterization and reconstruction via supervised learning. Acta Materialia, 2016, 103: 89-102.

[10] Brownlee, J. Machine learning mastery, 2014. URL:http://machinelearningmastery.com/discover-feature-engineering-howtoengineer-features-and-how-to-getgood-at-it

[11] Butler, K. T., Davies, D. W., Cartwright, H., Isayev,O., Walsh, A. Machine learning for molecular and materials science. Nature, 2018, 559(7715): 547.

[12] Chaker, Z., Salanne, M., Delaye, J. M., Charpentier, T.Fast and accurate predictions of NMR parameters in aluminosilicate glasses via Machine Learning, 2019.

[13] Chatterjee, D., Ghosh, S., Brady, P. R., Kapadia, S. J.,Miller, A. L., Nissanke, S., Pannarale, F.,2019.

[14] Chen, D., Mak, B. K. W. Multitask learning of deep neural networks for low-resource speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(7):1172-1183.

[15] Chen, X., Gupta, A. Webly supervised learning of convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, 2015:1431-1439.

[16] Cheng, M. Y., Hoang, N. D. A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads. Knowledge-Based Systems, 2015, 76: 256-263.

[17] Cheng, Y. Semi-supervised learning for neural machine translation. In Joint Training for Neural Machine Translation. Springer, Singapore, 2019: 25-40.

[18] Chikersal, P., Poria, S., Cambria, E. SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015: 647-651.

[19] Chong, B. S. A machine learning approach to detect surface features for automatic robot taping, 2019.

[20] Chu, S., Wagstaff, K., Bryden, G., Shvartzvald, Y.Automatic Detection of Microlensing Events in the Galactic Bulge using Machine Learning Techniques.In Astronomical Data Analysis Software and Systems XXVII, 2019, 523: 127.

[21] Claussen, N., Bernevig, B. A., Regnault, N. Detection of Topological Materials with Machine Learning. arXiv preprint arXiv:1910.10161, 2019.

[22] Conneau, A., Kiela, D., Schwenk, H., Barrault, L.,Bordes, A. Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364, 2017.

[23] Cruz, J. A., Wishart, D. S. Applications of machine learning in cancer prediction and prognosis.Cancer informatics, 2, 117693510600200030, 2006.

[24] Dawson, C. J., Molloy, C. L., Trim, C. M., Ganci Jr, J.M. U.S. Patent Application No. 15/952,320,2019.

[25] Dey, A. Machine learning algorithms: a review. International Journal of Computer Science and Information Technologies, 2016, 7(3): 1174-1179.

[26] Durai, V., Ramesh, S., Kalthireddy, D. Liver disease prediction using machine learning, 2019.

[27] Durand, T., Mordan, T., Thome, N., Cord, M. Wildcat: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 642-651.

[28] Foerster, J., Assael, I. A., de Freitas, N., Whiteson,S. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems, 2016:2137-2145.

[29] Forner, D., Ozcan, S., Bacon, D. Machine Learning Approach for National Innovation Performance Data Analysis, 2019.

[30] Fu, G. S., Levin-Schwartz, Y., Lin, Q. H., Zhang, D.Machine Learning for Medical Imaging.Journal of healthcare engineering, 2019.

[31] Gales, M. Introduction to machine learning, 2008.

[32] Gondia, A., Siam, A., El-Dakhakhni, W., Nassar, A.H. Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management,2019, 146(1):04019085.

[33] Greenberg, D. E., Kim, J., Zhan, X., Shelburne, S. A.,Shelburne, S. A., Aitken, S. L., Aitken, S. L. 1831.Machine Learning Approaches to Predicting Resistance in Pseudomonas aeruginosa. In Open Forum Infectious Diseases. Oxford University Press, 2019,6(Suppl 2): S42.

[34] Gu, S., Holly, E., Lillicrap, T., Levine, S. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE in ternational conference on robotics and automation (ICRA). IEEE, 2017: 3389-3396.

[35] Hamilton, I. Fraud Detection Through the Utilization of Machine Learning. 2019.

[36] Harutyunyan, H., Khachatrian, H., Kale, D. C.,Steeg, G. V., Galstyan, A. Multitask learning and benchmarking with clinical time series data. ArXiv preprint arXiv:1703.07771, 2017.

[37] Harwath, D., Torralba, A., Glass, J. Unsupervised learning of spoken language with visual context.In Advances in Neural Information Processing Systems, 2016: 1858-1866.

[38] He, B., Wei, M., Watts, D. R., Donohue, K. A., Tracey, K. L., Shen, Y. Detect slow slip events in ocean bottom pressure data using machine learning. Threshold, 2019, 68: 95-5.

[39] Hey, T., Butler, K., Jackson, S., Thiyagalingam, J.Machine Learning and Big Scientific Data.arXiv preprint arXiv:1910.07631, 2019.

[40] Hong, S., You, T., Kwak, S., Han, B. Online tracking by learning discriminative saliency map with convolutional neural network. In International conference on machine learning, 2015: 597-606.

[41] Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell,D. B., Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science, 2016,353(6301): 790-794.

[42] Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell,D. B., Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science, 2016,353(6301): 790-794.

[43] Johnson, R., Zhang, T. Semi-supervised convolutional neural networks for text categorization via region embedding. In Advances in neural information processing systems, 2015: 919-927.

[44] Khasawneh, K. N., Ozsoy, M., Donovick, C.,Abu-Ghazaleh, N., & Ponomarev, D. Ensemble learning for low-level hardware-supported malware detection. In International Symposium on Recent Advances in Intrusion Detection. Springer, Cham, 2015:3-25.

[45] King, A. J., Cooper, G. F., Clermont, G., Hochheiser,H., Hauskrecht, M., Sittig, D. F.,Visweswaran, S.Using Machine Learning to Selectively Highlight Patient Information. Journal of Biomedical Informatics,2019: 103327.

[46] Kipf, T. N., Welling, M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.

[47] Kudyshev, Z. A., Kildishev, A. V., Shalaev, V. M.,Boltasseva, A. Machine-Learning-Assisted Metasurface Design for High-Efficiency Thermal Emitter Optimization. arXiv preprint arXiv:1910.12741,2019.

[48] Kuznietsov, Y., Stuckler, J., Leibe, B. Semi-supervised deep learning for monocular depth map prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:6647-6655.

[49] Laradji, I. H., Alshayeb, M., Ghouti, L. Software defect prediction using ensemble learning on selected features. Information and Software Technology, 2015, 58: 388-402.

[50] Lavecchia, A. Machine-learning approaches in drug discovery: methods and applications. Drug discovery today, 2015, 20(3): 318-331.

[51] Law, M. T., Traboulsee, A. L., Li, D. K., Carruthers,R. L., Freedman, M. S., Kolind, S. H., Tam, R. Machine learning in secondary progressive multiple sclerosis: an improved predictive model for shortterm disability progression. Multiple Sclerosis Journal–Experimental, Translational and Clinical, 2019,5(4): 2055217319885983.

[52] Learned-Miller, E. G. Introduction to supervised learning. I: Department of Computer Science, University of Massachusetts, 2014.

[53] Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.R. Introduction to machine learning for brain imaging. Neuroimage, 2011, 56(2): 387-399.

[54] Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., Jurafsky, D. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541, 2016.

[55] Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., Yan, S.Towards computational baby learning: A weakly-supervised approach for object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2015: 999-1007.

[56] Lin, E., Tsai, S. J. Machine Learning in Neural Networks. In Frontiers in Psychiatry. Springer, Singapore, 2019: 127-137.

[57] Lison, P. An introduction to machine learning. Language Technology Group (LTG), 2015, 1(35).

[58] Liu, T., Yang, Y., Huang, G. B., Yeo, Y. K., Lin, Z.Driver distraction detection using semi-supervised machine learning. IEEE transactions on intelligent transportation systems, 2015, 17(4): 1108-1120.

[59] Liu, Y. J., Li, J., Tong, S., Chen, C. P. Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE transactions on neural networks and learning systems, 2016,27(7): 1562-1571.

[60] Liu, Y. Y., Welch, D., England, R., Stacey, J., Harbison, S. Forensic STR allele extraction using a machine learning paradigm. Forensic Science International: Genetics, 102194, 2019.

[61] Mairal, J. Incremental majorization-minimization optimization with application to large-scale machine learning. SIAM Journal on Optimization, 2015,25(2): 829-855.

[62] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015:4959-4962.

[63] Masood, H., Toe, C. Y., Teoh, W. Y., Sethu, V., Amal,R. Machine Learning for Accelerated Discovery of Solar Photocatalysts. ACS Catalysis, 2019.

[64] Mattos, D. I., Bosch, J., Olsson, H. H. Leveraging Business Transformation with Machine Learning Experiments. In International Conference on Software Business. Springer, Cham, 2019: 183-191.

[65] Misra, I., Shrivastava, A., Hebert, M. Watch and learn: Semi-supervised learning for object detectors from video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015:3593-3602.

[66] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M. G., Petersen, S. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529.

[67] Munro, R. J., Walker, C., Luger, S. K., Callahan, B.D., King, G. C., Tepper, P. A., Long, J. D. U.S. Patent Application No. 16/185,843, 2019.

[68] Murthy, A., Green, C., Stoleru, R., Bhunia, S., Swanson, C., Chaspari, T. Machine Learning-based Irrigation Control Optimization. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation.ACM, 2019: 213-222.

[69] Narayanaswamy, S., Paige, T. B., Van de Meent, J.W., Desmaison, A., Goodman, N., Kohli, P., Torr, P.Learning disentangled representations with semi-supervised deep generative models. In Advances in Neural Information Processing Systems, 2017: 5925-5935.

[70] Noroozi, M., Favaro, P. Unsupervised learning of visual representations by solving jigsaw puzzles.In European Conference on Computer Vision. Springer,Cham, 2016: 69-84 .

[71] Odena, A. Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583, 2016.

[72] Pagliardini, M., Gupta, P., Jaggi, M. Unsupervised learning of sentence embeddings using compositional n-gram features. arXiv preprint arXiv:1703.02507,2017.

[73] Peng, H., Thomson, S., Smith, N. A. Deep multitask learning for semantic dependency parsing. arXiv preprint arXiv:1704.06855, 2017.

[74] Pyati, V. U.S. Patent Application No. 15/949,107,2019.

[75] Rezende, D. J., Eslami, S. A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N. Unsupervised learning of 3d structure from images. In Advances in Neural Information Processing Systems, 2016:4996-5004.

[76] Sajan, K. K., Ramachandran, G. S., Krishnamachari, B. Enhancing Support for Machine Learning and Edge Computing on an IoT Data Marketplace.In Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. ACM, 2019: 19-24.

[77] Salamon, J., Bello, J. P. Unsupervised feature learning for urban sound classification. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015:171-175.

[78] Sanchez-Lengeling, B., Wei, J. N., Lee, B. K., Gerkin, R. C., Aspuru-Guzik, A., Wiltschko, A. B. Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules.arXiv preprint arXiv:1910.10685, 2019.

[79] Sanchez-Lengeling, B., Wei, J. N., Lee, B. K., Gerkin, R. C., Aspuru-Guzik, A., & Wiltschko, A. B.Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules. arXiv preprint arXiv:1910.10685, 2019.

[80] Schuld, M., Sinayskiy, I., Petruccione, F. An introduction to quantum machine learning.ContemporaryPhysics, 2015, 56(2): 172-185.

[81] Shen, W. U.S. Patent No. 10,467,339. Washington,DC: U.S. Patent and Trademark Office, 2019.

[82] Srivastava, N., Mansimov, E., Salakhudinov, R. Unsupervised learning of video representations using lstms. In International conference on machine learning, 2015: 843-852.

[83] Stoudenmire, E., Schwab, D. J. Supervised learning with tensor networks. In Advances in Neural Information Processing Systems, 2016: 4799-4807.

[84] Sun, Y., Tang, K., Minku, L. L., Wang, S., Yao, X.Online ensemble learning of data streams with gradually evolved classes. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(6):1532-1545.

[85] Tandia, A., Onbasli, M. C., Mauro, J. C. Machine Learning for Glass Modeling. In Springer Handbook of Glass. Springer, Cham, 2019: 1157-1192.

[86] Tu, Y., Lin, Y., Wang, J., Kim, J. U. Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput. Mater.Continua, 2018, 55(2): 243-254.

[87] Wang, L.Discovering phase transitions with unsupervised learning. Physical Review B, 2016,94(19):195105.

[88] Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang,G. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 2015, 149: 708-717.

[89] Wang, X., Gupta, A. Unsupervised learning of visual representations using videos. In Proceedings of the IEEE International Conference on Computer Vision,2015: 2794-2802.

[90] Yao, X., Han, J., Cheng, G., Qian, X., Guo, L. Yang,H. F., Lin, K., Chen, C. S. Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(2): 437-451.

[91] Yerima, S. Y., Sezer, S., Muttik, I. High accuracy android malware detection using ensemble learning. IET Information Security, 2015, 9(6): 313-320.

[92] Zhang, F., Du, B., Zhang, L., Xu, M. Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(9): 5553-5563

[93] Zhao, M., Chow, T. W., Zhang, Z., Li, B. Automatic image annotation via compact graph based semi-supervised learning. Knowledge-Based Systems, 2015,76: 148-165.

[94] Zhao, Y., Li, J., Yu, L. A deep learning ensemble approach for crude oil price forecasting. Energy Economics, 2017, 66: 9-16.

[95] Zoph, B., Le, Q. V. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578,2016.

[96] Ren, Z., Yan, J., Ni, B., Liu, B., Yang, X., Zha, H.Unsupervised deep learning for optical flow estimation. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.

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How to Cite

Udousoro, I. C. (2020). Machine Learning: A Review. Semiconductor Science and Information Devices, 2(2), 5–14. https://doi.org/10.30564/ssid.v2i2.1931

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Vol. 2 , Iss. 2 (October 2020)

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