(don't forget to insert it into the 'table of contents')
Classification
Classification is an attempt to label a set of unlabeled conditions (assign them to
classes) according to a set of conditions with known labels. Thus, the classification
process consists of two stages:
1) Training the classifier using the pre-labeled set of conditions, and a selected
subset of probes (regarded as the selected features).
2) Using
the
trained
classifier
to
label
other
conditions.
Currently, Expander only supports two class labels for each classification analysis.
Expander utilizes the following algorithms to generate and train classifiers and to
classify conditions accordingly:
KNN (K-Nearest Neighbors)
To train a classifier using this method, select Classification >> Train Classifier >>
KNN. The KNN parameter (number of nearest neighbors used) can be set as input,
or estimated by cross-validation (depending on the selection in the input dialog). For
more information regarding the algorithm, refer to the References section.
SVM (Support Vector Machine)
Select Classification >> Train Classifier >> SVM to train a classifier using this
method.
For more details regarding the SVM algorithm and the implementation utilized by
Expander see the References section.
The train set of conditions can be imported from an external file (see the Files Format
section for details) or defined manually, depending on the selection in the ‘Training
Set’ section of the classifier train dialog box. The selected features (probes subset)
can be defined in one of the following manners depending on the selection in the
‘Feature Set’ section of the classifier train dialog box:
a) Imported from a file (see the Files Format section for details).
b) Estimated using t-test, i.e. selecting the features which best separate the two
classes according to the train set.
c) Estimated using a correlation filter, i.e. searching for n probes which
demonstrate the best correlation to an artificial vector, representing the train
set classification.
d) All features
After training a classifier a classifier view is displayed. It contains, on its left pane,
information regarding the used classification method and parameters, as well as a
specification of the train set of conditions and their labels.
In order to classify a set of conditions using one of the pre-trained classifiers, select
Classification >> Classify. A dialog box will appear, allowing selection of one of the
open classifiers from a combo-box. The set of conditions to be classified can be
selected from a list containing all conditions in the data. After pressing ‘OK’ in the
dialog box, classification is performed and the results appear as a table in the right
pane of the relevant classifier view. The table contains, for each classified condition,
the assigned label, and a level of confidence.
References
KNN Classification algorithm: D. Aha, D. Kibler. Instance-based learning
algorithms. Machine Learning. 6:37-66, 1991.
SVM Classification algorithm:
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy. Improvements to
Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637649, 2001.
J. Platt: Machines using Sequential Minimal Optimization. In B. Schoelkopf and C.
Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector
Learning, 1998.