author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari and Paola Mello and
   Annamaria Nanetti},
  title = {Discovering Validation Rules from Micro-biological Data},
  journal = {New Generation Computing},
  year = {2003},
  volume = {21},
  number = {2},
  pages = {123--134},
  publisher = {Ohmsha, Ltd. and Springer},
  address = {Tokyo, \Japan},
  month = feb,
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamRigStoMelNan-NGC03.pdf},
  http = {http://www.springerlink.com/content/b816tm18j5715810},
  doi = {10.1007/BF03037630},
  copyright = {Ohmsha, Ltd. and Springer},
  abstract = {A huge amount of data is daily collected from clinical mi-
crobiology laboratories. These data concern the resistance or susceptibil-
ity of bacteria to tested antibiotics. Almost all microbiology laboratories
follow standard antibiotic testing guidelines which suggest antibiotic test
execution methods and result interpretation and validation (among them,
those annually published by NCCLS). Guidelines basically specify, for
each species, the antibiotics to be tested, how to interpret the results of
tests and a list of exceptions regarding particular antibiotic test results.
Even if these standards are quite assessed, they do not consider pecu-
liar features of a given hospital laboratory, which possibly influence the
antimicrobial test results, and the further validation process.
In order to improve and better tailor the validation process, we have
applied knowledge discovery techniques, and data mining in particular,
to microbiological data with the purpose of discovering new validation
rules, not yet included in NCCLS guidelines, but considered plausible and
correct by interviewed experts. In particular, we applied the knowledge
discovery process in order to find (association) rules relating to each other
the susceptibility or resistance of a bacterium to different antibiotics.
This approach is not antithetic, but complementary to that based on
NCCLS rules: it proved very effective in validating some of them, and
also in extending that compendium. In this respect, the new discovered
knowledge has lead microbiologists to be aware of new correlations among
some antimicrobial test results, which were previously unnoticed. Last
but not least, the new discovered rules, taking into account the history
of the considered laboratory, are better tailored to the hospital situation,
and this is very important since some resistances to antibiotics are specific
to particular, local hospital environments.},
  keywords = {Knowledge Discovery and Data mining, Microbiology, Knowledge Based Systems, Knowledge Elicitation}
  author = {Fabrizio Riguzzi},
  title = {Specification of the Application SuperSport with {ER}-{DFD}},
  institution = {Dipartimento di Ingegneria, Universit\`{a} di Ferrara},
  year = 2003,
  number = {CS-2003-01},
  month = jul,
  url = {http://ds.ing.unife.it/~friguzzi/Papers/CS-2003-01.pdf}
  author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
  title = {Belief Revision via {L}amarckian Evolution},
  journal = {New Generation Computing},
  abstract = {We present a system for performing belief revision in a
multi-agent environment.  The system is called GBR (Genetic
Belief Revisor) and it is based on a genetic algorithm. In this
setting, different individuals are exposed to different
experiences. This may happen because the world surrounding an
agent changes over time or because  we allow agents exploring
different parts of the world. The algorithm permits the exchange
of chromosomes from different agents and combines two different
evolution strategies, one based on Darwin's and the other  on
Lamarck's evolutionary theory. The algorithm therefore includes
also a Lamarckian operator that changes the memes of an agent in
order to improve their fitness. The operator is implemented by
means of a belief revision procedure that, by tracing logical
derivations, identifies the memes leading to contradiction.
Moreover, the algorithm comprises a special crossover mechanism
for memes in which a meme can be acquired from another agent only
if the other agent has ``accessed'' the meme, i.e. if an
application of the Lamarckian operator has read or modified the

Experiments have been performed on the $n$-queen problem and on a
problem of digital circuit diagnosis. In the case of the
$n$-queen problem, the addition of the Lamarckian operator in the
single agent case improves the fitness of the best solution. In
both cases the experiments show that the distribution of
constraints, even if it may lead to a reduction of the fitness of
the best solution, does not produce a significant reduction.},
  publisher = {Ohmsha, Ltd. and Springer},
  address = {Tokyo, \Japan},
  keywords = {Genetic_Algorithms,Theory_Revision},
  year = {2003},
  volume = {21},
  number = {3},
  month = aug,
  pages = {247--275},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamRigPer-NGC03.pdf},
  http = {http://www.springerlink.com/content/063764w6n3847825/},
  doi = {10.1007/BF03037475},
  copyright = {Ohmsha, Ltd. and Springer}
  author = {Evelina Lamma and Fabrizio Riguzzi and Andrea Stambazzi and Sergio Storari},
  title = {Improving the {SLA} algorithm using association rules},
  booktitle = {{AI*IA} 2003: Advances in Artificial Intelligence: 8th Congress of the Italian Association for Artificial Intelligence Pisa, Italy, September 23-26, 2003 Proceedings},
  editor = {Amedeo Cappelli and Franco Turini},
  abstract = {A bayesian network is an appropriate tool for working
with uncertainty and probability, that are typical of real-life
applications. In literature we find different approaches for
bayesian network learning. Some of them are based on search and
score methodology and the others follow an information theory
based approach. One of the most known algorithm for learning
bayesian network is the SLA algorithm. This algorithm constructs
a bayesian network by analyzing conditional independence
relationships among nodes. The SLA algorithm has three phases:
drafting, thickening and thinning. In this work, we propose an
alternative method for performing the drafting phase. This new
methodology uses data mining techniques, and in particular the
computation of a number of parameters usually defined in relation
to association rules, in order to learn an initial structure of a
bayesian network. In this paper, we present the BNL-rules
algorithm (Bayesian Network Learner with association rules) that
exploits a number of  association rules parameters to infer the
structure of a bayesian network. We will also present the
comparisons  between SLA and BNL-rules algorithms on learning
four bayesian networks. },
  year = {2003},
  month = sep,
  publisher = {Springer Verlag},
  address = {Heidelberg, \Germany},
  series = {{Lecture Notes on Artificial Intelligence}},
  volume = {2829},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  pages = {165--175},
  keywords = {Bayesian Networks Learning},
  issn = {0302-9743},
  doi = {10.1007/b13658},
  isbn = {3-540-20119-X},
  http = {http://dx.medra.org/10.1007/b13658},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamRigSta-AIIA03.pdf},
  copyright = {Springer}

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