Machine learning algorithms: a background artifact

  • Authors

    • J. Deepika
    • T. Senthil
    • C. Rajan
    • A. Surendar
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9214
  • Machine learning algorithms and types, supervised learning algorithms, unsupervised learning algorithms, R code, python script.

  • With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.  

  • References

    1. [1] Chang A, “R for Machine Learning, Prediction: Machine Learning and Statisticsâ€, MIT OpenCourseWare, (2012), pp.1-8.

      [2] Singh A, Thakur N & Sharma A, “A review of supervised machine learning algorithmsâ€, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), (2016), pp.1310-1315.

      [3] Archana S & Elangovan K, “Survey of Classification Techniques in Data Miningâ€, International Journal of Computer Science and Mobile Applications, Vol.2, (2014), pp.65-71.

      [4] Sun B, Chen S, Wang J & Chen H, “A robust multi-class AdaBoost algorithm for mislabelled noisy dataâ€, Knowledge-Based Systems, Vol.102, (2016), pp.87-102.

      [5] Chapelle O, Sindhwani V & Keerthi SS, “Optimization Techniques for Semi-Supervised Support Vector Machinesâ€, Journal of Machine Learning Research, Vol.9, (2013), pp.203–233.

      [6] Chen D, Tian Y & Liu X, “Structural nonparallel support vector machine for pattern recognitionâ€, Pattern Recognition, Vol.60, (2016), pp.296-305.

      [7] Blanco F, Ãvila JC, Jiménez GR, Carvalho A, Díaz AO & Bueno RM, “Online adaptive decision trees based on concentration inequalitiesâ€, Knowledge-Based Systems, Vol.104, (2016), pp.179-194.

      [8] Fatima M & Pasha M, “Survey of Machine Learning Algorithms for Disease Diagnosticâ€, Journal of Intelligent Learning Systems and Applications, Vol.9, (2017), pp.1-16.

      [9] Xu J, Wu Q, Zhang J & Tang Z, “Exploiting Universum data in AdaBoost using gradient descentâ€, Image and Vision Computing, Vol.32, (2014), pp.550-557.

      [10] Das K & Behera RN, “A Survey on Machine Learning: Concept, Algorithms and Applicationsâ€, International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, No.2, (2017), pp.1301-1309.

      [11] Miller LD & Soh LK, “Cluster-Based Boostingâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.27, (2015), pp.1491-1504.

      [12] Baig M, Awais MM & El-Alfy EM, “AdaBoost-based artificial neural network learningâ€, Neurocomputing, Vol.16, (2017), pp.22–41.

      [13] Praveena M & Jaiganesh V, “A Literature Review on Supervised Machine Learning Algorithms and Boosting Processâ€, International Journal of Computer Applications, Vol.169, No.8, (2017), pp.32-35.

      [14] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V & Vanderplas J, “Scikit-learn: Machine learning in Pythonâ€, Journal of Machine Learning Research, (2011), pp.2825-2830.

      [15] Xiao Q, Liang Y, Lu L, Yan S & Tai YW, “Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAsâ€, Proceedings of the 54th Annual Design Automation Conference, (2017).

      [16] Hiregoudar SB, Manjunath K & Patil KS, “A Survey: Research Summary on Neural Networksâ€, International Journal of Research in Engineering and Technology, Vol.03, Special Issue.03, (2014), pp.385-389.

      [17] Sharma V, Rai S & Dev A, “A Comprehensive Study of Artificial Neural Networksâ€, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, No.10, (2012), 278 -284.

      [18] Utkin V & Zhuk YA, “An one-class classification support vector machine model by interval-valued training dataâ€, Knowledge-Based Systems, Vol.120, (2017), pp.43-56.

      [19] Vijayarani S & Dhayanand S, “Liver Disease Prediction using SVM and Naïve Bayes Algorithmsâ€, International Journal of Science, Engineering and Technology Research, Vol.4, (2015), pp.816-820.

      [20] Wang J, Jebara T & Chang SF, “Semi-supervised learning using greedy max- cutâ€, Journal of Machine Learning Research, Vol.14, No.1, (2013), pp.771-800.

      [21] Peng X, Rafferty K & Ferguson S, “Building support vector machines in the context of regularized least squaresâ€, Neuro computing, Vol.211, (2016), pp.129-142.

      [22] Surendar, A., Arun, M., Basha, A.M. “Micro sequence identification of bioinformatics data using pattern mining techniques in FPGA hardware implementation†(2016), Asian Journal of Information Technology, 15 (1), pp. 76-81.

      [23] Panchumarthi, G.P., Surendar,A.â€A review article on Fin-FET based self-checking full addersâ€,(2017) Journal of Advanced Research in Dynamical and Control Systems, 9 (4), 8 p.

      [24] Priyanka Reddy, G.S., Surendar, A.â€A review article on performance comparison of CNTFET based full addersâ€,(2017) Journal of Advanced Research in Dynamical and Control Systems, 9 (4), pp. 9-20.

      [25] Selvi, N., Surendar, A. “Efficient power reduction and glitch free mux based digitally controlled delay lineâ€,(2015), International Journal of Applied Engineering Research, 10 (10), pp. 9655-9659.

      [26] Vishnu, S., Vignesh, S., Surendar, A.â€Design and implementation of ZETA micro-inverter for solar PV applicationâ€(2017), International Journal of Mechanical and Production Engineering Research and Development, 7 (4), pp. 215-222.

      [27] Lakshmi, K., Surendar, A. â€Verification of axiprotocol using system Verilogâ€, (2017), International Journal of Mechanical Engineering and Technology, 8 (5), pp. 588-595.

      [28] Surendar, A., Kavitha, M. “Secure patient data transmission in sensor networksâ€, (2017), Journal of Pharmaceutical Sciences and Research, 9 (2), pp. 230-232.

      [29] Surendar, A.â€FPGA based parallel computation techniques for bioinformatics applicationsâ€,(2017) International Journal of Research in Pharmaceutical Sciences, 8 (2), pp. 124-128.

      [30] Surendar, A.â€Evolution of gait biometric system and algorithms- A review†(2017) Biomedical and Pharmacology Journal, 10 (1), pp. 467-472.

      [31] Vimalkumar, M.N., Helenprabha, K., Surendar, A.â€Classification of mammographic image abnormalities based on emo and LS-SVM techniquesâ€,(2017) Research Journal of Biotechnology, 12 (1), pp. 35-40.

      [32] Manju, K., Sabeenian, R.S., Surendar, A.â€A review on optic disc and cup segmentationâ€,(2017) Biomedical and Pharmacology Journal, 10 (1), pp. 373-379.

      [33] Surendar, A., Rani, N.U.â€High speed data searching algorithms for DNA searchingâ€,(2016) International Journal of Pharma and Bio Sciences, 2016 (Special Issue), pp. 73-77.

      [34] Surendar, A., Arun, M.â€Efficient DNA sequence analysis for reduced gene selection using frequency analysisâ€, (2016) Journal of Chemical and Pharmaceutical Sciences, 9 (4), pp. 3367-3373.

      [35] Surendar, A., George, A.â€A real-time searching and sequencing assembly platform based on an FPGA implementation for Bioinformatics applicationsâ€,(2016) International Journal of Pharma and Bio Sciences, 7 (4), pp. B642-B647.

      [36] Surendar, A., Arun, M.â€FPGA based multi-level architecture for next generation DNA sequencingâ€,(2016) Biomedical Research (India), 2016, pp. S75-S79.

      [37] Surendar, A., Arun, M., Basha, A.M.â€Micro sequence identification of bioinformatics data using pattern mining techniques in FPGA hardware implementationâ€,(2016) Asian Journal of Information Technology, 15 (1), pp. 76-81.

      [38] Prabu, G., Surendar, A.â€Virus detection by using a pattern matching algorithm for network securityâ€,(2015) International Journal of Applied Engineering Research, 10 (10), pp. 9565-9569.

      [39] Surendar, A., Arun, M., Periasamy, P.S.â€Hardware based algorithms for bioinformatics applications - A surveyâ€,(2013) International Journal of Applied Engineering Research, 8 (6), pp. 745-754.

      [40] B. Saichandana, G. Rachana sri, A. Surendar and B. Suniltej “controlling of wall lamp using arduinoâ€, International Journal of Pure and Applied Mathematics, Volume 116 No. 24 2017, 349-354, ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)

      [41] A. Surendar and Usha Rani Nelakuditi, “Editorial - New Developments in Electronics, Cloud and IoTâ€, Electronic Government, An International Journal, Vol. 13, No. 4, 2017, ISSN online: 1740-7508 ISSN print: 1740-7494,pp -287-289

      [42] Surendar, A. "Improving Age Invariant Face Recognition System Using Facial Features." Research Journal of Pharmacy and Technology10.6 (2017): 1762-1766.

      [43] Sahu, Anil Kumar, Rashid Sheikh, and A. Surendar. "Ultra low power design approach of asynchronous delta sigma modulator." International Journal of Engineering & Technology 8.1.1 (2018): 84-87.

      [44] Surendar, A., M. Kavitha, and V. Saravanakumar. "Proactive model based testing and evaluation for component-based systems." International Journal of Engineering & Technology 8.1.1 (2018): 74-77.

  • Downloads

  • How to Cite

    Deepika, J., Senthil, T., Rajan, C., & Surendar, A. (2017). Machine learning algorithms: a background artifact. International Journal of Engineering & Technology, 7(1.1), 143-149. https://doi.org/10.14419/ijet.v7i1.1.9214