@article{LamMelRig04-CJ-IJ, author = {Evelina Lamma and Paola Mello and Fabrizio Riguzzi}, title = {A System for Measuring Function Points from an {ER}-{DFD} Specification}, journal = {The Computer Journal}, abstract = {We present a tool for measuring the Function Point software metric from the specification of a software system expressed in the form of an Entity Relationship diagram plus a Data Flow Diagram (ER-DFD). First, the informal and general Function Point counting rules are translated into rigorous rules expressing properties of the ER-DFD. Then, the rigorous rules are translated into Prolog. The measures given by the system on a number of case studies are in accordance with those of human experts.}, publisher = {Oxford University Press}, address = {Oxford, \UK}, keywords = {Software Engineering, Software Metrics, Function Points}, year = {2004}, volume = {47}, number = {3}, pages = {358--372}, month = may, pdf = {http://mcs.unife.it/~friguzzi/Papers/fun.pdf}, http = {http://comjnl.oxfordjournals.org/cgi/reprint/47/3/358}, issn = {0010--4620}, doi = {10.1093/comjnl/47.3.358}, copyright = {Evelina Lamma, Paola Mello and Fabrizio Riguzzi, licensed exclusively to The British Computer Society} }

@inproceedings{LamRigSto04-IPMU04-IC, author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari}, title = {Improving the K2 Algorithm Using Association Rules Parameters}, booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems ({IPMU}2004), Perugia, 4-9 July 2004}, editor = { B. Bouchon-Meunier and G. Coletti and R. R. Yager}, abstract = {A Bayesian network is an appropriate tool to work with a sort of uncertainty and probability, that are typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian network. In this paper we want to present an extension of K2 called K2-rules that exploits a parameter normally defined in relation to association rules for learning Bayesian networks. The experiments performed show that K2-rules improves K2 with respect to both the quality of the learned network and the execution time. }, year = {2004}, month = jul, publisher = {Editrice Universit\`{a} La Sapienza}, address = {\Rome, \Italy}, pages = {1667--1674}, url = {http://ipmu2004.dipmat.unipg.it/}, keywords = {Bayesian Networks Learning}, pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSto-IPMU04.pdf} }

@inproceedings{LamRigSto04-ECAI04-IC, author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari}, title = {Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm}, booktitle = {Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, including Prestigious Applicants of Intelligent Systems, PAIS 2004, Valencia, Spain, August 22-27, 2004}, editor = {Ramon Lopez de Mantaras and Lorenza Saitta}, abstract = {A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian network. In this paper we present two extensions of K2 called K2-Lift and K2-X2 that exploit two parameters normally defined in relation to association and correlation rules for learning Bayesian networks. The experiments performed show that K2-Lift and K2-X2 improve K2 with respect to both the quality of the learned network and the execution time.}, year = {2004}, month = aug, publisher = {{IOS} Press}, address = {Amsterdam, \TheNetherlands}, pages = {500-504}, keywords = {Bayesian Networks Learning}, isbn = {1-58603-452-9}, isbn = {9781586034528}, pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSto-ECAI04.pdf}, url = {http://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0500.pdf}, series = {Frontiers in Artificial Intelligence and Applications}, issn = {0922-6389}, volume = {110} }

@inproceedings{Rig04-ILP04-IC, author = {Fabrizio Riguzzi}, title = {Learning Logic Programs with Annotated Disjunctions}, booktitle = {Inductive Logic Programming: 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004. Proceedings}, editor = {A Srinivasan and R. King}, abstract = {Logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for integrating probabilistic reasoning and logic programming. In this paper we propose an algorithm for learning LPADs. The learning problem we consider consists in starting from a sets of interpretations annotated with their probability and finding one (or more) LPAD that assign to each interpretation the associated probability. The learning algorithm first finds all the disjunctive clauses that are true in all interpretations, then it assigns to each disjunct in the head a probability and finally decides how to combine the clauses to form an LPAD by solving a constraint satisfaction problem. We show that the learning algorithm is correct and complete.}, year = {2004}, month = sep, publisher = {Springer Verlag}, address = {Heidelberg, \Germany}, keywords = {Logic Programming, Inductive Logic Programming, Probabilistic Logic Programming}, series = {Lecture Notes in Artificial Intelligence}, volume = {3194}, pages = {270--287}, note = {The original publication is available at \url{http://www.springerlink.com}}, pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ILP04.pdf}, http = {http://link.springer.com/chapter/10.1007%2F978-3-540-30109-7_21}, isbn = {3-540-22941-8}, issn = {0302-9743}, doi = {10.1007/978-3-540-30109-7_21}, copyright = {Springer}, wos = {WOS:000223999400021}, scopus = {2s2.022944492698} }

@inproceedings{Rig04-DISCCHALL-IW, author = {Fabrizio Riguzzi}, title = {Classification and visualization on the hepatitis dataset}, booktitle = {ECML/PKDD 2004 Discovery Challenge, Pisa, 20-24 September 2004}, editor = {Petr Berka and Bruno Cremilleux}, abstract = {In this paper we address goals 2 and 3 of those proposed by the donors of the Hepatitis dataset, namely to evaluate whether it is possible to estimate the stage of liver fibrosis from the results of examinations, and to evaluate the effectiveness of the interferon therapy. Goal 2 was addressed by learning various classifiers that predict the value of fibrosis from the values of examinations other than the biopsy. Unfortunately, the best accuracy obtained was only 50.6 \%, up only 2.1 \% from the performance of the default classifier, thus showing that replacing biopsies is still very hard if not impossible. As regards goal 3, we have plotted the distribution of the values of the difference in fibrosis and in activity before and after the interferon therapy. The plots show that the therapy actually reduces the level of activity but not the level of fibrosis. Moreover, we have also plotted the distribution of the values of the difference of GOT before and after the therapy. The graph shows that a moderate reduction of GOT is obtained.}, year = {2004}, month = sep, keywords = {Classification, Visualization}, url = {http://lisp.vse.cz/challenge/ecmlpkdd2004/final/riguzzi.ps}, pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-DISCCHALL04.pdf} }

@article{RigSto04-NJ, author = {Fabrizio Riguzzi and Sergio Storari}, title = {La Sedicesima Conferenza Europea di Intelligenza Artificiale ({ECAI})}, journal = {Intelligenza Artificiale}, abstract = {Relazione sulla Sedicesima Conferenza Europea di Intelligenza Artificiale ({ECAI})}, year = {2004}, volume = {Anno {I}}, number = {4}, month = dec, issn = {1724-8035}, pages = {57--60}, publisher = {Associazione Italiana per l'Intelligenza Artificiale {AI*IA}}, address = {Bari, \Italy}, pdf = {http://mcs.unife.it/~friguzzi/Papers/RigSto-IA04.pdf} }

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