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"Basic Feature Selection Process"
smackdown33
Member Posts: 19 Maven
Hi ive been trying to run the following XML code with varying heap space arrangements and nothing has come to fruition. I keep getting GC errors or heap space errors. The dataset consists of 1300 examples with 7200 attributes but after about 10secs the previously mentioned errors arise. Can someone please help.
<operator name="Root" class="Process" expanded="yes">
<operator name="ArffExampleSource" class="ArffExampleSource">
<parameter key="data_file" value="G:\Postgrad\Java\NetBean Projects\ImageUtil\lutImages\newPNGImages\10Colours\Features\features2.arff"/>
<parameter key="datamanagement" value="short_sparse_array"/>
</operator>
<operator name="FS" class="FeatureSelection" expanded="yes">
<operator name="FSChain" class="OperatorChain" expanded="yes">
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="Learner" class="LibSVMLearner">
<list key="class_weights">
</list>
</operator>
<operator name="ApplierChain" class="OperatorChain" expanded="yes">
<operator name="Applier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="Evaluator" class="Performance">
</operator>
</operator>
</operator>
<operator name="ProcessLog" class="ProcessLog">
<list key="log">
<parameter key="generation" value="operator.FS.value.generation"/>
<parameter key="performance" value="operator.FS.value.performance"/>
</list>
</operator>
</operator>
</operator>
</operator>
<operator name="Root" class="Process" expanded="yes">
<operator name="ArffExampleSource" class="ArffExampleSource">
<parameter key="data_file" value="G:\Postgrad\Java\NetBean Projects\ImageUtil\lutImages\newPNGImages\10Colours\Features\features2.arff"/>
<parameter key="datamanagement" value="short_sparse_array"/>
</operator>
<operator name="FS" class="FeatureSelection" expanded="yes">
<operator name="FSChain" class="OperatorChain" expanded="yes">
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="Learner" class="LibSVMLearner">
<list key="class_weights">
</list>
</operator>
<operator name="ApplierChain" class="OperatorChain" expanded="yes">
<operator name="Applier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="Evaluator" class="Performance">
</operator>
</operator>
</operator>
<operator name="ProcessLog" class="ProcessLog">
<list key="log">
<parameter key="generation" value="operator.FS.value.generation"/>
<parameter key="performance" value="operator.FS.value.performance"/>
</list>
</operator>
</operator>
</operator>
</operator>
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0
Answers
that's correct. Unfortunately the current implementation of the feature selection needs way too much memory. Please take a look at this forum thread, where we had the complete discussion, yet. http://rapid-i.com/rapidforum/index.php/topic,1089.0.html
Greetings,
Sebastian
thanks for the response, ive read the other thread, but it didnt really get me anywhere. I was wondering do you or anyone out there know the best way to select a reduced feature set from the 7200 attributes I have and then train an SVM using the reduced feature set with the computer limitations i have.
Did you try the evolutionary Feature Selection?
Greetings,
Sebastian
is that the evolutionary feature aggregation which you are referring to. Its the only feature selection method that i can find that is "evolutionary".
Thanks again for your help.
beside from buying the metioned plugin? Hmm. You could do an evolutionaryWeighting, this should switch of unimportant attributes by giving them weight 0. Hope that will help you.
Greetings,
Sebastian
can you PM me the price of the plugin, thanks.