biblio_i03.bib

@INBOOK{annr,
  AUTHOR = {C. Furlanello and M. Serafini and S. Merler and G. Jurman},
  EDITOR = {D.C. Wunsch II and M. Hasselmo and K. Venayagamoorthy and D. Wang},
  TITLE = {Advances in Neural Network Research: IJCNN 2003},
  CHAPTER = {An accelerated procedure for recursive feature ranking on
        microarray data},
  PUBLISHER = {Elsevier},
  YEAR = {2003}
}

@ARTICLE{furlanello2004control,
  AUTHOR = {C. Furlanello and M. Serafini and S. Merler and G. Jurman},
  TITLE = {CONTROL OF SELECTION BIAS IN MICROARRAY DATA ANALYSIS},
  YEAR = {2003},
  JOURNAL = {Minerva Biotecnologica},
  VOLUME = 15,
  PAGES = { 217--222},
  NUMBER = 4
}

@ARTICLE{furlanello2003entropy,
  AUTHOR = {C. Furlanello and M. Serafini and S. Merler and G. Jurman},
  TITLE = {Entropy-{B}ased {G}ene {R}anking without {S}election {B}ias for the {P}redictive {C}lassification of {M}icroarray {D}ata},
  YEAR = {2003},
  JOURNAL = {BMC Bioinformatics},
  NUMBER = {4},
  VOLUME = {},
  PAGES = {54},
  PDF = {http://www.biomedcentral.com/1471-2105/4/54},
  ABSTRACT = {We describe the E--RFE method for gene ranking,
useful for the identification of markers in the predictive
classification of array data. The method supports a practical modeling
scheme designed to avoid the construction of classification rules
based on the selection of very small gene subsets (an effect known as
the selection bias, in which too optimistic predictive errors are
estimated due to testing on samples already considered in the feature
selection process).  \newline

\textbf{Results:} With E--RFE, we speed up the recursive feature e
limination (RFE) with SVM classifiers by eliminating chunks of
uninteresting genes using to an entropy measure of the SVM weights
distribution. An optimal subset of genes is selected according to a
two-strata model evaluation procedure: modeling is replicated by an
external stratified-partition resampling scheme, and, within each run,
an internal K-fold cross-validation is used for E--RFE ranking. Also,
the optimal number of genes can be estimated according to the
saturation of Zipf's law profiles.\newline

\textbf{Conclusions:} Without a decrease of classification accuracy,
E--RFE allows a speed-up factor of 100 with respect to standard RFE,
however, improving on alternative parametric RFE reduction strategies.
A process for gene selection and error estimation is, thus, made
practical, ensuring control of the selection bias, and providing
additional diagnostic indicators of gene importance.}
}

@ARTICLE{merler2003anaccelerated,
  AUTHOR = {C. Furlanello and M. Serafini and S. Merler and G. Jurman},
  TITLE = { An accelerated procedure for recursive feature ranking on
        microarray data},
  YEAR = {2003},
  JOURNAL = {Neural Networks},
  NUMBER = {5--6},
  VOLUME = {16},
  PAGES = {641--648},
  ABSTRACT = { We describe a new wrapper algorithm for fast feature ranking in
  classification problems.  The \mbox{E--RFE} (Entropy-based Recursive
  Feature Elimination) method eliminates chunks of uninteresting
  features according to the entropy of the weights distribution of a
  SVM classifier. With specific regard to DNA microarray datasets, the
  method is designed to support computationally intensive model
  selection in classification problems in which the number of features
  is much larger than the number of samples. We test \mbox{E--RFE} on
  synthetic and real data sets, comparing it with other SVM-based
  methods. The speed-up obtained with \mbox{E--RFE} supports predictive
  modeling on high dimensional microarray data.}
}

@INPROCEEDINGS{furlanello2003gis,
  AUTHOR = {C. Furlanello and M. Neteler and S. Merler and S. Menegon and S. Fontanari and A. Donini and A. Rizzoli and C. Chemini},
  TITLE = {{GIS} and the {R}andom {F}orest {P}redictor: {I}ntegration in {R} for {T}ick-borne {D}isease {R}isk {A}ssessment},
  BOOKTITLE = {Proceedings of the 3rd International Workshop on Distributed Statistical Computing},
  PUBLISHER = {},
  EDITOR = {K. Hornik and F. Leisch},
  PAGES = {},
  YEAR = {Vienna, Austria, March 20-22, 2003},
  ABSTRACT = { We discuss how sophisticated machine learning methods may
 be rapidly integrated within a GIS for the development of new
 approaches in landscape epidemiology. A multitemporal predictive map
 is obtained by modeling in \texttt{R}\xspace, analyzing geodata and
 digital maps in \texttt{GRASS}\xspace, and managing biodata samples
 and weather data in \texttt{PostgreSQL}\xspace. In particular, we
 present a risk mapping system for tick-borne diseases, applied to
 model the risk of exposure to Lyme borreliosis and tick-borne
 encephalitis (TBE) in Trentino, Italian Alps.},
  LINK = {http://mpa.itc.it/papers/furlanello2003gis.pdf}
}

@ARTICLE{merler2003automatic,
  AUTHOR = {S. Merler and C. Furlanello and B. Larcher and A. Sboner},
  TITLE = {Automatic Model Selection in Cost-sensitive Boosting},
  YEAR = {2003},
  JOURNAL = {Information Fusion},
  NUMBER = {1},
  VOLUME = {4},
  PAGES = {3--10},
  ABSTRACT = {  This paper introduces SSTBoost, a predictive classification
  methodology designed to target the accuracy of a modified boosting
  algorithm towards required sensitivity and specificity constraints.
  The SSTBoost method is demonstrated in practice for the automated
  medical diagnosis of cancer on a set of skin lesions (42 melanomas
  and 110 naevi) described by geometric and colorimetric features. A
  cost-sensitive variant of the AdaBoost algorithm is combined with a
  procedure for the automatic selection of optimal cost parameters.
  Within each boosting step, different weights are considered for
  errors on false negatives and false positives, and differently
  updated for negatives and positives. Given only a target region in
  the ROC space, the method also completely automates the selection of
  the cost parameters ratio, tipically of uncertain definition. On the
  cancer diagnosis problem, SSTBoost outperformed in accuracy and
  stability a battery of specialized automatic systems based on
  different types of multiple classifier combinations and a panel of
  expert dermatologists.  The method thus can be applied for the early
  diagnosis of melanoma cancer or in other problems in which an
  automated cost-sensitive classification is required.},
  LINK = {http://mpa.itc.it/papers/merler2003automatic.pdf}
}

@INPROCEEDINGS{furlanello2002gene,
  AUTHOR = {C. Furlanello and M. Serafini and S. Merler and G. Jurman},
  TITLE = {Gene selection and classification by {E}ntropy-based {R}ecursive {F}eature {E}limination},
  BOOKTITLE = {International Joint Conference on Neural Networks},
  PUBLISHER = {},
  EDITOR = {},
  PAGES = {3077--3082},
  YEAR = { Portland, Oregon, July 20-24, 2003.},
  ABSTRACT = {We analyse E-RFE (Entropy-based Recursive Feature
Elimination), a new wrapper algorithm for fast feature ranking in
classification problems.  The E-RFE method operates the elimination of
chunks of uninteresting features according to the entropy of the
weights distribution of a SVM classifier.  The method is designed to
support computationally intensive model selection in classification
problems in which the number of features is much larger than the
number of samples.  We proofread the elimination procedure on
synthetic data sets, and we demonstrate the applicability of E-RFE for
the identification of biomedically important genes in predictive
classification of microarray data.}
}

@ARTICLE{mitasovaneteler2003,
  AUTHOR = {H. Mitasova and M. Neteler},
  TITLE = {Free {G}eneral-purpose {GIS}. {A} {G}eographic {R}esources {A}nalysis
           {S}upport {S}ystem},
  JOURNAL = {{GIM} {I}nternational},
  NUMBER = {11},
  VOLUME = {17},
  YEAR = 2003,
  PAGES = {40-43},
  ABSTRACT = {The Geographic Resources Analysis, GRASS, a general purpose
   GIS originally developed by U.S. Army Corps of Engineers Laboratory, has
   grown into one of the main components of Open Source and Free Software
   geospatial computational infrastructure.  Current developments led by
   international team of programmers, focus on improving the 2D and 3D raster
   and vector data processing and analysis tools and 3D visualization
   capabilities in the wake of publishing of the code under GPL in 1999.
   Applications in the area of epidemiology, coastal management and water flow
   modelling provide a snapshot of the capabilities.},
  LINK = {http://www.gim-international.com/}
}

@INPROCEEDINGS{AntoniolClusteringGA2003,
  AUTHOR = {G. Antoniol and M. Di Penta and M. Neteler},
  TITLE = {Moving to Smaller Libraries via Clustering and Genetic
             Algorithms},
  BOOKTITLE = {CSMR 2003, 7th IEEE European Conference on Software
                 Maintenance and Reengineering},
  YEAR = {2003},
  PAGES = {307--316},
  URL = {http://www.scoda.unisannio.it/~antoniol/publications/papers/csmr2003lib.pdf},
  ABSTRACT = {There may be  several reasons to reduce a software  system
      to its bare bone removing the extra fat introduced during development
      or evolution.  Porting the software system on embedded devices or
      palmtops are just two examples.

      This paper presents an approach to re-factoring libraries with the aim
      of reducing the memory requirements of executables.  The approach is
      organized in two steps.  The first step defines an initial solution
      based on clustering methods, while the subsequent phase refines the
      initial solution via genetic algorithms.

      In  particular, a  novel genetic  algorithm approach,  considering the
      initial clusters as the starting population, adopting a
      knowledge-based mutation function and a multi-objective fitness
      function, is proposed.

      The approach  has been applied  to several medium and  large-size open
      source software systems such as GRASS, KDE-QT, Samba and MySQL,
      allowing to effectively produce smaller, loosely coupled libraries,
      and to reduce the memory requirement for each application.}
}

@ARTICLE{raghavan2003GRASS,
  AUTHOR = {V. Raghavan and K. Kita and K. Iwao and M. Neteler},
  TITLE = {Open source {GIS} {GRASS} for developing spatial data infrastructure
         - {P}resent status and future potential},
  JOURNAL = {Journal of Information Science and Technology Association},
  NUMBER = {53},
  VOLUME = {4},
  YEAR = 2003,
  PAGES = {216-222},
  ABSTRACT = {This article outlines the salient features and current state
    of development of the Open Source GIS GRASS. We discuss the concepts and
    issues related to the development of GRASS that represents the only full
    fledged, multi-platform GIS available as OSS. Further, we highlight the
    potential of GRASS GIS in developing spatial data infrastructure and put
    forth a proposal for establishing a GRASS Consortium to support, nurture and
    accelerate furher developments.},
  LINK = {http://www.infosta.or.jp/journal/back2003e.html}
}