Nicola Barile
Nicola joined the ADA group as a PhD student in 2007 to work on the AMOC project, which is focused on the development of learning algorithms for the construction of monotone classifiers from data. He received his Master's degree in Computer Science from the University of Bari, Italy in 2006 after defending his thesis titled "Transductive Classification: A Relational Approach"; after that, he worked on an EU-funded project on Web Usage Mining. Nicola's research interests are in Data Mining and Knowledge Discovery, Multi-relational Data Mining, Web Mining, and Machine Learning.

Selected Refereed Publications

Barile, N. & Feelders, A. Nonparametric Ordinal Classification with Monotonicity Constraints. In: MoMo 2009, ECML PKDD'09 Workshop on Learning Monotone Models from Data, 2009.
van de Kamp, R., Feelders, A. & Barile, N. Isotonic Classification Trees. In: N. Adams, C. Robardet, A. Siebes & J.-F. Boulicaut (eds.) Advances in Intelligent Data Analysis VIII (IDA'09), Springer, pp 405-416, 2009.
Barile, N. & Feelders, A. Nonparametric Monotone Classification with MOCA. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), 2008.
Ceci, M., Appice, A., Barile, N., Malerba, D. Transductive Learning from Relational Data. In: Proceedings of the International Conference on Machine Learning and Data Mining (MLDM'07), 2007.
Appice, A., Barile, N., Ceci, M., Malerba, D., Singh, R.P. Mining Geospatial Data in a Transductive Setting. In: Proceedings of the eighth International Conference on Data, Text and Web Mining and their Business Applications including Information Engineering Management (DMIE'07), 2007.