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Classification
setup (Setup
button)
Setup has only one
independent control - block's name. Name should not
contain white spaces nor "\" symbols - these
characters may lead to mistakes in some cases.
Other controls are grouped in three tabs:
classification setup, forward mapping and filter
setup and triggering setup.
Apply
button saves all parameters but doesn't start the
calculations;
Go!
button saves parameters and starts calculations, it
is enabled only in
Manual
trigger mode.
-
classification setup
Classification
algorithms:
kNN:
k
nearest neighbors; algorithm calculates outputs
basing on the number of "signal" events among
the
k
training events with the smallest distance to the
classified event. It is simple, relatively fast and well
known algorithm, but don't expect outstanding
results.
Bayes: Class probability density
distributions are estimated as a sums of the
gaussian functions centered over events from the
training set. Then conditional likelihood is
calculated directly from the
formula. Width of the gaussian functions is
the user defined parameter. Algorithm is
extremely time-consuming but a bit more reliable
results than kNN.
LBGBins:
Space of the training vectors is quantized with
LBG algorithm to obtain
N
representative vectors. These vectors become
centroids of
N
sectors with uniform conditional likelihood
calculated as a ratio of "signal" to
"background" training
events in the given sector (bin). Classified
events are assigned to the nearest centroid and,
in result, use its pre-calculated likelihood.
Remember: in
most cases it is necessary to use some
preprocessing (N(0,1)
normalization at least); this is true for all
implemented algorithms.
clear input: Input
DataSets
will be cleared
after processing.
clear output:
Output
DataSets
will be cleared before processing. If
unchecked - new events will be attached to the
existing ones.
- forward
mapping and filter setup
When classification of events from input
DataSets
is done,
theirs contents is sent to the output
DataSets
according to the forward mapping specifications.
Destination events in each output
DataSet
may be composed in a different way. Select a
DataSet
in the
Output Data Set
list and put desired
formatting
strings
for destination event vectors. In the image
above there is a simple example:
-
destination event input vector will be composed
of the first and second element of the source
event input vector;
- destination event
output vector will be created with the length=1
and it will contain first element of the
classification output vector;
- destination
event target and not-used vector will not be
allocated.
Events stored in the output
DataSets
can be filtered (only events with the t1=0.95
will be sent to the "signal"
DataSet
in the example from the image above).
- triggering setup
If
Manual
mode is selected,
Go!
button is enabled and it releases
calculations. If
Internal
mode is chosen -
Source
button is enabled and it allows to select source
of the trigger through the common
Connection Add
dialog window (Go!
button is disabled); processing starts when one
of the selected sources finished its own task.
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