Abstract
Machine Learning (ML) has been used efficiently in applications across multiple domains. As a consequence, there is a growing interest in ML techniques and artifacts that facilitate its use. However, most of them are aimed at researchers and experienced users. In addition, few artifacts provide more than ready-made algorithms. In this work, we present a framework capable of delivering ready-to-use ML algorithms, as well as the code to be reused by form applications that use ML algorithms. We also show an example where we used the framework to build two form applications. The results show that the framework is able to reduce approximately 50% of the effort when building a new application. In addition, the framework allows to include new state-of-the-art ML algorithms in an easy way, as well as it provides a simple flow control that assists inexperienced users in the use of ML algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 44(1.2), 206–226 (2000)
Wen, J., et al.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54(1), 41–59 (2012)
Chen, N., Hoi, S.C.H., Xiao, X.: Software process evaluation: a machine learning framework with application to defect management process. Empir. Softw. Eng. 19, 1531–1564 (2013)
Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst. Appl. 97, 205–227 (2018)
Peng, Y., Yin, H.: Markov random field based convolutional neural networks for image classification. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 387–396. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_42
Zhang, D., Zhou, L.: Discovering golden nuggets: data mining in financial application. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 34(4), 513–522 (2004)
Apte, C.: The role of machine learning in business optimization. In: Proceedings of the 27th International Conference on Machine Learning, pp. 1–2 (2010)
Jiang, Y., Zhu, G., Lin, L.: Research of dengue fever prediction in san juan, puerto rico based on a KNN regression model. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 146–153. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_17
Zhan, L., Zhang, J., Yang, Q., Lin, Y.: Applying random forest to drive recommendation. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 470–480. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_51
Hsieh, J.-L., Sun, C.-T.: Building a player strategy model by analyzing replays of real-time strategy games. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (2008)
Sonnenburg, S., et al.: The need for open source software in machine learning. J. Mach. Learn. Res. 8, 2443–2466 (2007)
Collobert, R., Bengio, S., Mariéthoz, J.: Torch: a modular machine learning software library. Idiap (2002)
Gashker, M.: Waffles: a machine learning toolkit. J. Mach. Learn. Res. 12, 2383–2387 (2011)
Konkol, M.: Brainy: a machine learning library. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8468, pp. 490–499. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07176-3_43
Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18, 1–5 (2013)
Bischl, B., et al.: Machine learning in R. J. Mach. Learn. Res. 17, 1–5 (2016)
Lauer, F.: MLweb: a toolkit for machine learning on the web. Neurocomputing. 282, 74–77 (2018)
Costa, E., et al.: A framework for building web mining applications in the world of blogs: a case study in product sentiment analysis. Expert Syst. Appl. 39, 4813–4834 (2012)
Fayad, M., Schmidt, D.C.: Object-oriented application frameworks. Commun. ACM 40(10), 32–38 (1997)
Calderon-Vilca, H.D., Wun-Rafael, W.I., Miranda-Loarte, R.: Simulation of suicide tendency by using machine learning. In: 2017 36th International Conference of the Chilean Computer Science Society (SCCC) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Aguiar, G., Vilain, P. (2018). A Framework for Form Applications that Use Machine Learning. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_80
Download citation
DOI: https://doi.org/10.1007/978-3-030-03493-1_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03492-4
Online ISBN: 978-3-030-03493-1
eBook Packages: Computer ScienceComputer Science (R0)