"Basic Feature Selection Process"
New Altair Community Member
Updated by Jocelyn
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>
Find more posts tagged with
Sort by:
1 - 7 of
71
New Altair Community Member
OPHi 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.
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.
New Altair Community Member
Hi,
Did you try the evolutionary Feature Selection?
Greetings,
Sebastian
Did you try the evolutionary Feature Selection?
Greetings,
Sebastian
New Altair Community Member
OPHi 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.
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.
New Altair Community Member
OPHi, below is the setup i most recently used and Im still not getting anything from it, keeps running out of memory after at most 1min. If you can help me get this sorted out, you will be a life save Sebastian, thanks for your 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"/>
</operator>
<operator name="EvolutionaryFeatureAggregation" class="EvolutionaryFeatureAggregation" expanded="yes">
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="JMySVMLearner" class="JMySVMLearner">
</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 name="ProcessLog" class="ProcessLog">
<list key="log">
</list>
</operator>
</operator>
</operator>
New Altair Community Member
Hi,
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
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
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