"Classification performance of one-class SVM"
New Altair Community Member
Updated by Jocelyn
Posted in SourceForge forum at http://sourceforge.net/forum/forum.php?thread_id=2185902&forum_id=390413:
Does anybody have any example/project they are willing to share showing the
use of one-class libSVM; I've been playing around with rapidminer for the past
day (which is amazing btw-- tnx!) but using the one-class and modifying any
of the parameters (gamma, coef, C, nu, etc.) does not affect the classification
result which is 100%. I've even modified the data on purpose (to mess it up)
and no luck on seeing any changes.
I've also exported the data (as an arff file) and imported it in to Weka and
the results were more reasonable (changing gamma from 0.01 to 0.07 changed the
classification correctness to ~88%). (This might be off topic, but if anybody
knows how to do PCA on one-class data set in Weka that would be also helpful)
Here is the simplified model xml file:
Does anybody have any example/project they are willing to share showing the
use of one-class libSVM; I've been playing around with rapidminer for the past
day (which is amazing btw-- tnx!) but using the one-class and modifying any
of the parameters (gamma, coef, C, nu, etc.) does not affect the classification
result which is 100%. I've even modified the data on purpose (to mess it up)
and no luck on seeing any changes.
I've also exported the data (as an arff file) and imported it in to Weka and
the results were more reasonable (changing gamma from 0.01 to 0.07 changed the
classification correctness to ~88%). (This might be off topic, but if anybody
knows how to do PCA on one-class data set in Weka that would be also helpful)
Here is the simplified model xml file:
Any help is very much appreciated! Thanks!
<operator name="Root" class="Process" expanded="yes">
<parameter key="logfile" value="/home/d/rm_workspace/log.log"/>
<parameter key="resultfile" value="/home/d/rm_workspace/results.res"/>
<operator name="ArffExampleSource" class="ArffExampleSource">
<parameter key="data_file" value="file.arff"/>
<parameter key="label_attribute" value="class"/>
</operator>
<operator name="Normalization" class="Normalization">
<parameter key="min" value="-1.0"/>
</operator>
<operator name="PCA" class="PCA">
</operator>
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="LibSVMLearner" class="LibSVMLearner">
<parameter key="cache_size" value="150"/>
<list key="class_weights">
</list>
<parameter key="coef0" value="0.03125"/>
<parameter key="confidence_for_multiclass" value="false"/>
<parameter key="degree" value="1"/>
<parameter key="gamma" value="0.01"/>
<parameter key="nu" value="9.765625E-4"/>
<parameter key="svm_type" value="one-class"/>
</operator>
<operator name="OperatorChain" class="OperatorChain" expanded="yes">
<operator name="ModelApplier (2)" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="Performance" class="Performance">
</operator>
</operator>
</operator>
</operator>
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New Altair Community Member
In the original I did have the mode applier (see attached) -- i just posted the wrong model -- sorry :-\
in any case I also tried with and even simpler example:
[attachment deleted by admin]
in any case I also tried with and even simpler example:
where the arff file is:
<?xml version="1.0" encoding="US-ASCII"?>
<process version="4.2">
<operator name="Root" class="Process" expanded="yes">
<operator name="ArffExampleSource" class="ArffExampleSource" breakpoints="after">
<parameter key="data_file" value="/home/d/rm_workspace/a.arff"/>
<parameter key="label_attribute" value="class"/>
</operator>
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="LibSVMLearner" class="LibSVMLearner">
<list key="class_weights">
</list>
<parameter key="svm_type" value="one-class"/>
</operator>
<operator name="OperatorChain" class="OperatorChain" expanded="yes">
<operator name="ModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="Performance" class="Performance">
</operator>
</operator>
</operator>
</operator>
</process>
and the class prediction is 100%
relation simple
@attribute value numeric
@attribute class {a}
@data
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
1,a
...
-1,a
-1,a
-1,a
[attachment deleted by admin]
To transform the data you need to add ModelApplier immediately after the PCA step.