Bridging the gap between human knowledge and machine learning

  1. ALVARADO-PÉREZ, Juan Carlos 1
  2. PELUFFO-ORDÓÑEZ, Diego H. 2
  3. THERÓN, Roberto 3
  1. 1 Universidad de Salmanca Universidad Mariana
  2. 2 Universidad Cooperativa de Colombia
    info

    Universidad Cooperativa de Colombia

    Bogotá, Colombia

    ROR https://ror.org/04td15k45

  3. 3 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Año de publicación: 2015

Volumen: 4

Número: 1

Páginas: 54-64

Tipo: Artículo

DOI: 10.14201/ADCAIJ2015415464 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Objetivos de desarrollo sostenible

Resumen

Nowadays, great amount of data is being created by several sources from academic, scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning -which is holistic, flexible and parallel- may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided.

Referencias bibliográficas

  • Agrawal, R., Srikant, R., & others. (1994). Fast algorithms for mining association rules. En Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487–499).
  • Aguilar, D. A. G., Guerrero, C. S., & Penalvo, R. T. S. and F. G. (2010). Visual Analytics to Support E-learning. http://doi.org/10.5772/7932
  • Alonso, F., Martínez, L., Pérez, A., & Valente, J. P. (2012). Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned. Expert Systems with Applications, 39(8), 7524-7535. http://doi.org/10.1016/j.eswa.2012.01.133
  • Alvarado-Pérez, J. C., & Murillo, S. (s. f.). Knowledge discovery in databases from a perspective of intelligent in-formation visualization.
  • Alvarado-Pérez, J. C., & Peluffo-Ordó?ez, D. H. (2015). Artificial and Natural Intelligence Integration. En S. Omatu, Q. M. Malluhi, S. R. González, G. Bocewicz, E. Bucciarelli, G. Giulioni, & F. Iqba (Eds.), Distributed Computing and Artificial Intelligence, 12th International Conference (pp. 167-173). Springer International Publishing. Recuperado a partir de http://link.springer.com/chapter/10.1007/978-3-319-19638-1_19
  • Alvarez, L. A. (s. f.). Aplicación de minería de datos a estudios históricos prosopográficos, 38.
  • Ball, P. (2012). Why Society is a Complex Matter: Meeting Twenty-first Century Challenges with a New Kind of Science (2012.a ed.). Springer.
  • Bertini, E., & Lalanne, D. (2009). Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. En Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (pp. 12–20).
  • Brachman, R., Khabaza, T., Hill, M., Kloesgen, W., & Augustin, S. (1996). An Overview of Issues in Developing and Knowledge Discovery Industrial Data Mining Applications, 89-95.
  • Butz, A., Fisher, B., Christie, M., Krüger, A., Olivier, P., & Therón, R. (2009). Smart Graphics. Proc. of 10th Inter-national Salamanca, España: Springer. Recuperado a partir de http://www.amazon.ca/Smart-Graphics-International-Symposium-Proceedings/dp/364202114X
  • Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. Knowledge and data Engineering, IEEE Transactions on, 8(6), 866–883.
  • Cook, K., Earnshaw, R., & Stasko, J. (2007). Guest Editors’ Introduction: Discovering the Unexpected. IEEE Com-puter Graphics and Applications, 27(5), 15-19. http://doi.org/10.1109/MCG.2007.126
  • Dai, W., & Hu, P. (2014). Research on personalized behaviors recommendation System based on cloud computing. Telkomnika, 12(2), 1480-1486. http://doi.org/10.11591/telkomnika.v12i2.3443
  • Díaz, J. R. F. (2011). El impacto de las redes sociales en la propiedad intelectual. REDES.
  • Díaz, N. V., & García, I. S. (2002). ?` Pensabas que emocionarse era sencillo? Las emociones como fenómenos bio-lógicos, cognoscitivos y sociales. Revista Puertorriqueña de Psicología, 13(1), 1.
  • Dietterich, T. G. (1997). Machine-learning research: Four current directions. AI Magazine, 18(4), 97-136.
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM, 39(11), 27–34. http://doi.org/10.1145/240455.240464
  • Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in Knowledge Discovery and Data Mining. The MIT Press. Recuperado a partir de http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0262560976
  • Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2007, 1–16.
  • Gibson, J. J. (2014). The Ecological Approach to Visual Perception: Classic Edition. Psychology Press.
  • Hirji, K. K. (1999). Discovering data mining: from concept to implementation. SIGKDD Explor. Newsl., 1(1), 44–45. http://doi.org/10.1145/846170.846181
  • Huang, M.-J., Chen, M.-Y., & Lee, S.-C. (2007). Integrating data mining with case-based reasoning for chronic dis-eases prognosis and diagnosis. Expert Systems with Applications, 32(3), 856-867. http://doi.org/10.1016/j.eswa.2006.01.038
  • Huang, M.-J., Tsou, Y.-L., & Lee, S.-C. (2006). Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19(6), 396-403. http://doi.org/10.1016/j.knosys.2006.04.003
  • Imielinski, T., & Mannila, H. (1996). A database perspective on knowledge discovery. Communications of the ACM, 39(11), 58–64.
  • Kai Puolamäki, Alessio Bertone, Roberto Therón, Otto Huisman, Jimmy Johansson, Silvia Miksch, … Salvo Rinzivillo. (2010). Chapter 4 in Mastering The Information Age – Solving Problems with Visual Analytics. En Mastering the Information Age Solving Problems with Visual Analytics (Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis and Florian Mansmann). Germany.
  • Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual Analytics: Defini-tion, Process, and Challenges. En A. Kerren, J. T. Stasko, J.-D. Fekete, & C. North (Eds.), Information Vis-ualization (pp. 154-175). Springer Berlin Heidelberg. Recuperado a partir de http://link.springer.com/chapter/10.1007/978-3-540-70956-5_7
  • Kerber, E. S. B. L. R. (s. f.). Integrating Inductive and Deductive Database Mining.
  • Kim, K., & Lee, J. (2014). Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recognition, 47(2), 758-768. http://doi.org/10.1016/j.patcog.2013.07.022
  • Koh, L. C., Slingsby, A., Dykes, J., & Kam, T. S. (2011). Developing and Applying a User-Centered Model for the Design and Implementation of Information Visualization Tools. En 2011 15th International Conference on Information Visualisation (IV) (pp. 90-95). http://doi.org/10.1109/IV.2011.32
  • Kononenko, I., & Kukar, M. (2007). Machine Learning and Data Mining. Elsevier.
  • Kutz, J. (2013). Data-driven modeling and scientific computing: Methods for Integrating Dynamics of Complex Sys-tems and Big Data. Oxford University Press.
  • Liu, Y. (2014). A knowledge discovery method based on Web information retrieval. En WIT Transactions on Infor-mation and Communication Technologies (Vol. 46 VOLUME 1, pp. 537-544). http://doi.org/10.2495/ISME20130701
  • López, J. M. ., & Herrero, J. G. (2004). Técnicas de Análisis de Datos. Universidad Carlos III, Madrid.
  • Matheus, C. J., Chan, P. K., & Piatetsky-shapiro, G. (1993). Systems for Knowledge Discovery in Databases. IEEE Transactions On Knowledge And Data Engineering, 5, 903–913.
  • Mena, J. (2003). Investigative Data Mining for Security and Criminal Detection. Butterworth-Heinemann.
  • Mitchell, T. M. (1997). Machine Learning (1.a ed.). McGraw-Hill Science/Engineering/Math.
  • Ortigosa, A., Carro, R. M., & Quiroga, J. I. (2014). Predicting user personality by mining social interactions in Face-book. Journal of Computer and System Sciences, 80(1), 57-71. http://doi.org/10.1016/j.jcss.2013.03.008
  • Peluffo-Ordónez, D. H., Alvarado-Pérez, J. C., Lee, J. A., & Verleysen, M. (2015). Geometrical homotopy for data visualization. En European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
  • Peluffo-Ordóñez, D. H., Alvarado-Pérez, J. C., & Castro-Ospina, A. E. (2015). On the Spectral Clustering for Dy-namic Data. En J. M. F. Vicente, J. R. Álvarez-Sánchez, F. de la P. López, F. J. Toledo-Moreo, & H. Adeli (Eds.), Bioinspired Computation in Artificial Systems (pp. 148-155). Springer International Publishing. Re-cuperado a partir de http://link.springer.com/chapter/10.1007/978-3-319-18833-1_16
  • Peluffo-Ordóñez, D. H., Lee, J. A., & Verleysen, M. (2014). Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding. En T. Villmann, F.-M. Schleif, M. Kaden, & M. Lange (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (pp. 65-74). Springer International Publishing. Recuperado a partir de http://link.springer.com/chapter/10.1007/978-3-319-07695-9_6
  • Pethuru, R. (2014). Data Visualization: Creating Mind´s Eye. En Handbook of Research on Cloud Infrastructures for Big Data Analytics. IGI Global.
  • Phua, C., Lee, V., Smith, K., & Gayler, R. (2012). A Comprehensive Survey of Data Mining-based Fraud Detection Research. Computers in Human Behavior, 28(3), 1002-1013. http://doi.org/10.1016/j.chb.2012.01.002
  • Ras, Z. W., Tsumoto, S., & Zighed, D. A. (2008). Mining Complex Data: ECML/PKDD 2007 Third International Workshop, MDC 2007, Warsaw, Poland, September 17-21, 2007, Revised Selected Papers. Springer Science & Business Media.
  • Riquelme, J. C., Ruiz, R., & Gilbert, K. (2006). Mineria de datos: Conceptos y tendencias. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, (029), 11–18.
  • Roselli, M. (2011). Maduración cerebral y desarrollo cognoscitivo. Revista Latinoamericana de Ciencias Sociales, Niñez y Juventud, 1(1). Recuperado a partir de http://revistaumanizales.cinde.org.co/index.php/Revista-Latinoamericana/article/view/336
  • Theron, R., & Fontanillo, L. (2015). Diachronic-information visualization in historical dictionaries. Information Visualization, 14(2), 111–136.
  • Timarán Pereira, R. (2011). Arquitecturas de Integración del Proceso de Descubrimiento de Conocimiento con Sistemas de Gestión de bases de datos: un Estado del Arte.
  • Timaran, R. (2005). Nuevas primitivas sql para el descubrimiento de conocimiento en arquitecturas fuertemente acopladas con un sistema de gestion de bases de datos (Doctoral). Universidad del Valle, Santiago de Cali, Colombia.
  • Torres Ponjuán, D. (2009). Aproximaciones a la visualización como disciplina científica. ACIMED, 20(6), 161–174.
  • Tufféry, S. (2011). Data Mining and Statistics for Decision Making. John Wiley & Sons.
  • Turk-Browne, N. B. (2013). Functional interactions as big data in the human brain. Science, 342(6158), 580–584.
  • Wang, Y., & Li, Q. (2014). Review on studies and advances of machine learning approaches. Telkomnika, 12(2), 1487-1494. http://doi.org/10.11591/telkomnika.v12i2.3635
  • Wang, Y., & Li, Q. (2014). Review on the Studies and Advances of Machine Learning Approaches. ?KOMNIKA Indonesian Journal of Electrical Engineering, 12(2), 1487–1494.
  • Weiss, S. M., & Kulikowski, C. A. (1991). Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. M. Kaufmann Publishers.
Fuente de los datos: Dialnet