CompareML: A Novel Approach to Supporting Preliminary Data Analysis Decision Making

• Antonio Jesús Fernández-García ^[1] ; Juan Carlos Preciado ^[2] ; Álvaro E. Prieto ^[2] ; Fernando Sánchez-Figueroa ^[2] ; Juan D. Gutiérrez ^[2]
  1. [1] Universidad Internacional de La Rioja

     Universidad Internacional de La Rioja

     Logroño, España

     
  2. [2] Universidad de Extremadura

     Universidad de Extremadura

     Badajoz, España

     
• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 7, Nº. 4, 2022, págs. 225-238
• Idioma: inglés
• DOI: 10.9781/ijimai.2021.08.001
• Enlaces
  • Texto completo (pdf)
• Resumen

  • Over the last years, works related to accessible technologies have increased both in number and in quality. This work presents a series of articles which explore different trends in the field of accessible video games for the blind or visually impaired. Reviewed articles are distributed in four categories covering the following subjects:

    (1) video game design and architecture, (2) video game adaptations, (3) accessible games as learning tools or treatments and (4) navigation and interaction in virtual environments. Current trends in accessible game design are also analysed, and data is presented regarding keyword use and thematic evolution over time. As a conclusion, a relative stagnation in the field of human-computer interaction for the blind is detected. However, as the video game industry is becoming increasingly interested in accessibility, new research opportunities are starting to appear

• Referencias bibliográficas
  • I. H. Witten, E. Frank, M. A. Hall, “Introduction to weka,” in Data Mining: Practical Machine Learning Tools and Techniques (Third Edition),...
  • S. Lang, F. Bravo-Marquez, C. Beckham, M. Hall, E. Frank, “Wekadeeplearning4j: A deep learning package for weka based on deeplearning4j,”...
  • J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek,...
  • M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, B. Wiswedel, “Knime - the konstanz information miner:...
  • I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, T. Euler, “Yale: Rapid prototyping for complex data mining tasks,” in Proceedings of the...
  • A. Jovic, K. Brkic, N. Bogunovic, “An overview of free software tools for general data mining,” in 2014 37th International Convention on Information...
  • X. He, K. Zhao, X. Chu, “Automl: A survey of the state-of- the-art,” Knowledge-Based Systems, vol. 212, p. 106622, 2021, doi: https://doi....
  • H. Song, P. Flach, “Efficient and robust model benchmarks with item response theory and adaptive testing,” International Journal of Interactive...
  • Microsoft, “Powerbi automated machine learning.” https://docs.microsoft. com/en-us/power-bi/transform-model/dataflows/dataflows-machinelearning-integration....
  • M. Ali, PyCaret: An open source, low-code machine learning library in Python, July 2020. PyCaret version 2.3.
  • Google, “Cloud automl.” https://cloud.google.com/automl. Online; last accessed 2 April 2021.
  • H. Robles-Berumen, A. Zafra, H. M. Fardoun, S. Ventura, “Leac: An efficient library for clustering with evolutionary algorithms,” KnowledgeBased...
  • D. Charte, F. Herrera, F. Charte, “Ruta: Implementations of neural autoencoders in r,” Knowledge-Based Systems, vol. 174, pp. 4 – 8, 2019,...
  • E. Real, C. Liang, D. R. So, Q. V. Le, “Automl-zero: Evolving machine learning algorithms from scratch,” 2020.
  • C. M. University, “Turi graphlab create.” https://turi.com/. Online; last accessed 2 April 2021.
  • G. van Rossum, the Python Software Foundation, “Python programming language.” https://www.python.org/. Online; last accessed 2 April 2021.
  • F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas,...
  • R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013.
  • A. Gupta, K. Ghanshala, R. C. Joshi, “Machine learning classifier approach with gaussian process, ensemble boosted trees, svm, and linear...
  • A. J. Fernández-García, L. Iribarne, A. Corral, J. Criado, J. Z. Wang, “A recommender system for component- based applications using machine...
  • A. J. Fernández-García, R. Rodríguez-Echeverría, J. C. Preciado, J. M. C. Manzano, F. Sánchez-Figueroa, “Creating a recommender system to...
  • T. H.-Y. Chiu, C. Wu, R. C. C.-H. Chen, “A generalized wine quality prediction framework by evolutionary algorithms,” International Journal...
  • K. M. Ting, Confusion Matrix, pp. 260–260. Boston, MA: Springer US, 2017.
  • A. Leff, J. T. Rayfield, “Web-application development using the model/ view/controller design pattern,” in Proceedings Fifth IEEE International...
  • W. McKinney, “pandas: a foundational python library for data analysis and statistics,” Python for High Performance and Scientific Computing,...
  • S. Hellegouarch, CherryPy Essentials: Rapid Python Web Application Development Design, Develop, Test, and Deploy Your Python Web Applications...
  • M. Bohanec, V. Rajkovič, “Knowledge acquisition and explanation for multi-attribute decision,” in 8th International Workshop Expert Systems...
  • D. Dua, C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml.
  • A. Tsanas, A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning...
  • J. Lewis, M. Fowler, “Microservices: a definition of this new architectural term.” http://martinfowler.com/articles/microservices.html, 2014.