A MATLAB version of our new classification algorithm with guaranteed
sensitivity and specificity has been released.
main feature of this classification algorithm is that it is
i.e., the sensitivity and specificity of the trained
classifier can be tested and certified by means of a rigorous
statistical method without the need of an independent test set.
Therefore, strange as it may seem, if you trust math (and your data
i.i.d.), then you need no validation set: all of your precious data can
be used in the training phase!
algorithm allows the user to control the sensitivity-specificity balance
by means of two input parameters.
new algorithm uses a simpler construction than its noble ancestor GEM,
which makes it easier to analyse and super-easy to implement.
immediately on your problem, and let
us know! :)
By downloading files from this website, you are accepting the following
We (the licensee) understand that the
GEM package is
supplied "as is", without expressed or implied warranty.
We agree on the following:
- The licensers do not have any
obligation to provide any maintenance or consulting help with respect
- The licensers neither have any
the use of classifiers built through GEM-BALLS, nor for the correctness
- We will only use GEM-BALLS for
purposes. This implies that neither GEM-BALLS nor any part of
code should be used or modified for any commercial software product. REFERENCE
cite the following paper (bibtex) when referring to
"A New Classification Algorithm With Guaranteed Sensitivity and
Specificity for Medical Applications",
by A. Carè, F.A. Ramponi, M.C. Campi. IEEE Control
Systems Letters, vol. 2, no. 3, pp. 393-398, July 2018.
(pdf copy here)
(the slides of a presentation at AUTOMATICA.IT 2019 can be found here)
Type >>help traingemballs
at the MATLAB prompt for a general description of the MATLAB
Here you can find
another MATLAB example where a pool of GEM-BALLS classifiers are built
from the same training set. Using many
GEM-BALLS classifiers together might considerably improve performance -
we have been currently researching on this:
"A study on majority-voting classifiers
with guarantees on the probability of error"
by A. Carè, M.C. Campi, F.A.
Ramponi, S. Garatti, A.T.J.R. Cobbenhagen
IFAC World Congress 2020 (pdf copy
"Novel bounds on the probability of
misclassification in majority voting: leveraging the majority size",
by A.T.J.R Cobbenhagen, A. Carè, M.C. Campi, F.A. Ramponi,
D.J. Antunes, W.P.M.H. Heemels. IEEE
Control Systems Letters, vol. 5, no. 5, pp. 1513-1518, 2020. https://doi.org/10.1109/LCSYS.2020.3040961 (pdf
IMPLEMENTATION OF ENSAMBLE GEM-BALLS
has published a classification scheme that combines
many GEM-BALLS classifiers. Here you can find the
code. Here you can find the
Here you are
an instance of a GEM-BALLS classifier (red=1, white=0) that was trained
argued that GEM-BALLS classifiers bear similarities with some
Boccioni's sculptures. Shall we
start talking about Boccioni
classifiers? (In Italian, "boccioni" also means "big