
Stefano Merler; Cesare Furlanello; Barbara Larcher; Andrea Sboner,
Automatic model selection in costsensitive boosting,
in «INFORMATION FUSION»,
vol. 4,
n. 1,
2003
, pp. 3 
10

Stefano Merler; Bruno Caprile; Cesare Furlanello,
BiasVariance Control via Hard Points Shaving,
in «INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE»,
vol. 18,
2003

Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
in «BMC BIOINFORMATICS»,
2003
, pp. 54 
73

Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
An accelerated procedure for recursive feature ranking on microarray data,
ADVANCES IN NEURAL NETWORK RESEARCH: IJCNN 2003,
Elsevier,
2003
, pp. 641 
648

Cesare Furlanello; Markus Neteler; Stefano Merler; Stefano Menegon; A. Fontanari; Angela Donini; Annapaola Rizzoli; C. Chemini,
Distributed Statistical Computing,
2003
, (Distributed Statistical Computing,
Vienna, Austria,
21/03/2003  22/03/2003)

Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
Control of selection bias in microarray data analysis,
Biotecnologica. A journal on Biotechnology and Molecular Biology,
2003
, pp. 217
222

Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
Gene selection and classification by entropybased recursive feature elimination,
Proceedings of the International Joint Conference on Neural Networks 2003,
2003
, pp. 3077
3082

Bruno Caprile; Stefano Merler; Cesare Furlanello,
Exact Bagging with kNearest Neighbour Classifiers,
A formula is derived for the exact computation of Bagging classifiers when the base model adopted is kNearest Neighbour (kNN). The formula, that holds in any dimension and does not require the extraction of bootstrap replicates, proves that Bagging cannot improve 1Nearest Neighbour. It also proves that, for k > 1, Bagging has a smoothing effect on kNN. Convergence of empirically bagged kNN predictors to the exact formula is also considered. Efficient approximations to the exact formula are derived, and their applicability to practical cases is illustrated,
2003

Stefano Merler; Cesare Furlanello,
Machine learning on historic air photographs for mapping risk of unexploded bombs,
2003

Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
An accelerated procedure for recursive feature ranking on microarray data,
We describe a new wrapper algorithm for fast feature ranking in classification problems. The ERFE (Entropybased 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 ERFE on synthetic and real data sets, comparing it with other SVMbased methods. The speedup obtained with ERFE supports predictive modeling on high dimensional microarray data,
2003