Maria Martinez-Ballesteros received the M.Sc. degree in Computer Engineering from the University of Seville, Spain, in 2009 and the Ph.D. degree in Computer Science from the University of Seville in 2012. Since 2009 she has been with the Department of Computer Science, University of Seville, where she is currently Assistant Professor. Her primary areas of interest are data mining, machine learning techniques, association rules and evolutionary computation.

1. Martínez-Ballesteros M, Troncoso A, Martínez-Álvarez F,  Riquelme JC. Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering 17 (3), 227-242, 2010. 

2. Martínez-Ballesteros M, Martínez-Álvarez F, Troncoso A, Riquelme JC. An Evolutionary Algorithm to Discover Quantitative Association Rules in Multidimensional Time Series. Soft Computing Vol. 15(10), pp. 2065-2084, 2011.

3. Martínez-Ballesteros M, Salcedo-Sanz S,  Riquelme JC, Casanova-Mateo C, Camacho JL. Evolutionary Association Rules for Total Ozone Content Modeling from Satellite Observations. Chemometrics and Intelligent Laboratory Systems, Vol. 109 (2), pp. 217- 227, 2011.

4. M. Martínez-Ballesteros, I .A. Nepomuceno-Chamorro, J. C. Riquelme. Discovering gene association networks by multi-objective evolutionary quantitative association rules. Journal of Computer and System Sciences, Vol. 80, pp. 118-136, 2014.

5. M. Martínez-Ballesteros, F. Martínez-Álvarez, A. Troncoso, J. C. Riquelme. Selecting the best measures to discover quantitative association rules Neurocomputing. Neurocomputing. Vol. 126, pp. 3-14, 2014.

6. M. Martínez-Ballesteros. Discovering quantitative association rules: A novel approach based on evolutionary algorithms. AI Communications. Accepted, In Press, 2013.