The Future of Machine Learning and Predictive Analytics

  • Ali Reza SamanpourEmail author
  • André Ruegenberg
  • Robin Ahlers


The history of artificial intelligence shows us that there has been a gradual and evolutionary development within the special aspects of computational sciences that underlie the technologies prevalent in machine learning. Most of the technologies consist of methods defined by so‐called computational intelligence, including neural networks, evolutionary algorithms and fuzzy systems. Data mining topics have also become more significant, due to the rapid growth in the quantities of data now available (Big Data), combined with having to face the same challenges encountered in the IoT (Internet of Things). The question now is: How can computers be made to do what needs to be done without anyone prescribing how it should be done? Nowadays, a whole range of providers offers frameworks for machine learning. Some of them allow us to use machine learning tools in the cloud. This option is mainly provided by the big players like Microsoft Azure ML, Amazon Machine Learning, IBM Bluemix and Google Prediction API, to name but a few.

Machine learning algorithms extract high‐level, complex abstractions as data representations by means of a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simple abstractions formulated in the preceding level in the hierarchy. Deep learning is a subarea of machine learning, and could even be described as a further development of this. While traditional machine learning algorithms rely on solid model groups for recognition and classification, deep learning algorithms develop and create their own new model levels within the neural networks independently. New models do not need to be repeatedly developed and implemented manually for each new set of data based on different structures, as would be the case for classic machine learning algorithms. The advantage of deep learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and uncategorized.


  1. 1.
    Y. D. W. &. C. Q. Yu, The impact of social and conventional media on firm equity value: A sentiment analysis approach, Decision Support Systems, 55.Google Scholar
  2. 2.
    I. Witten, E. Frank and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Burlington: Morgan Kaufmann Publishers, 2011.Google Scholar
  3. 3.
    WinfWiki, “Analyse Text Mining mit R,” 2014. [Online]. Available: [Accessed 14 08 2016].Google Scholar
  4. 4.
    A. Sharafi, Knowledge Discovery in Databases. Eine Analyse des Änderungsmanagements in der Produktentwicklung, Wiesbaden: Springer Gabler, 2012.Google Scholar
  5. 5.
    F. Pimenta, D. Obradovic, R. Schirru and S. &. D. A. Baumann, Automatic Sentiment, 2010.Google Scholar
  6. 6.
    B. Pang and L. &. V. S. Lee, “Thumbs up? Sentiment classification using machine learning techniques,” in Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002.Google Scholar
  7. 7.
    F. Nielsen and M. &. H. L. Etter, Real-time monitoring of sentiment in business related Wikipedia articles, 2013.Google Scholar
  8. 8.
    P. &. L. M. Turney, “Measuring praise and criticism: Inference of semantic orientation from association,” in ACM Transactions on Information Systems, 2003.Google Scholar
  9. 9.
    C. Manzei, L. Schleupner and R. Heinze, Industrie 4.0 im internationalen Kontext. Kernkonzepte, Ergebnisse, Trends, Berlin: VDE VERLAG GMBH, 2016.Google Scholar
  10. 10.
    G. Webb, “Naive Bayes,” in Encyclopedia of Machine Learning, Springer Verlag, 2010, p. 713.Google Scholar
  11. 11.
    C. R. P. &. S. H. Manning, Introduction to Information Retrieval, Cambridge University Press, 2008.CrossRefzbMATHGoogle Scholar
  12. 12.
    X. Zhang, “Support Vector Machines,” in Encyclopedia of Machine Learning, Springer Verlag, 2010, pp. 941–946.CrossRefGoogle Scholar
  13. 13.
    D. Jansen, Sentiment Classification, Duisburg: Universität Duisburg-Essen, 2008.Google Scholar
  14. 14.
    A. Ratnaparkhi, “Maximum Entropy Models,” in Encyclopedia of Machine Learning, Springer Verlag, 2010, pp. 647–651.Google Scholar
  15. 15.
    M. Schoenherr, “Ohne Verstand ans Ziel,” 2016. [Online]. Available: [Accessed 08 01 2016].Google Scholar
  16. 16.
    L. A. Zadeh, Fuzzy Sets, Information and Control, Elsevier Inc., 1965.zbMATHGoogle Scholar
  17. 17.
    R. Rojas, Therorie der Neuronalen Netze, Springer-Lehrbuch, 1993.CrossRefGoogle Scholar
  18. 18.
    J. R. Koza, Genetic Programming, MIT Press, 1992.zbMATHGoogle Scholar
  19. 19.
    R. Kruse, C. Borgelt, F. Klawonn et al., Computational Intelligence. Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze, Wiesbaden: Vieweg + Teubner Verlag, 2011.zbMATHGoogle Scholar
  20. 20., “50 Jahre Künstliche-Intelligenz-Forschung,” 2016. [Online]. Available: [Accessed 28 07 2016].Google Scholar
  21. 21.
    P. McCorduck, Machines who Think: A Personal Inquiry Into the History and Prospects of Artificial Intelligence, San Francisco: W. H. Freeman, 1979.Google Scholar
  22. 22.
    P. Koenig, “Künstliche Intelligenz,” 2007. [Online]. Available: [Accessed 09 08 2016].Google Scholar
  23. 23.
    A. Turing, Computing Machinery and Intelligence, 236 Hrsg., Bd. 59, 1950, pp. 433–460.Google Scholar
  24. 24.
    P. Ford, “Unsere letzte Erfindung?,” 2016. [Online]. Available: [Accessed 09 08 2016].Google Scholar
  25. 25.
    Wikipedia, “RoboCup,” 2016. [Online]. Available: [Accessed 08 01 2016].Google Scholar
  26. 26.
    E. Chin, “The Future of Fraud Fighting,” Sift science, 2015. [Online]. Available: [Accessed 08 01 2016].Google Scholar
  27. 27.
    S. Bahrampour, N. Ramakrishnan, L. Schott and M. Shah, Comparative Study of Deep Learning Software Frameworks, 2015.Google Scholar
  28. 28.
    Microsoft, “Internet der Dinge,” Azure IoT Suite, 2016. [Online]. Available: [Accessed 24 08 2016].Google Scholar
  29. 29.
    S. Fernández Arregui, S. Jiménez Celorrio and T. de la Rosa Turbides, “Improving Automated Planning with Machine Learning,” in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, New York, IGI Global, 2010.Google Scholar
  30. 30.
    P. Garica Laencina, J. Morales-Sanches, R. Verdu-Monedero and et al., “Classification with Incomplete Data,” in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, New York, IGI Global, 2010.Google Scholar
  31. 31.
    Herschel, R.T. and Jones, N.E., “Knowledge management and business intelligence: the importance of integration”, Journal of Knowledge Management, Vol. 9 No. 4, pp. 45‐55, 2005. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Ali Reza Samanpour
    • 1
    Email author
  • André Ruegenberg
    • 1
  • Robin Ahlers
    • 1
  1. 1.University of Applied Sciences South WestphaliaIserlohnGermany

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