Learning process for Vote module
inthewoods
New Altair Community Member
Hey all - I'm a newbie so please excuse what may be a stupid question. I'm trying to put together a simple test of an Ensemble Learning system using Adaboost. Here's what I've got so far:
Any help greatly appreciated!
I get an error on the Vote module which is asking for a Base Learner - but I'm not clear on what a Base Learner would be for a Vote. I'm sure it'll seem obvious when I see it but I'm stuck so far.
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.0.11" expanded="true" name="Process">
<process expanded="true" height="298" width="605">
<operator activated="true" class="retrieve" compatibility="5.0.11" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="SPY_multi_data_v1"/>
</operator>
<operator activated="true" class="series:windowing" compatibility="5.0.2" expanded="true" height="76" name="Windowing" width="90" x="179" y="30">
<parameter key="horizon" value="1"/>
<parameter key="window_size" value="1"/>
<parameter key="create_label" value="true"/>
<parameter key="label_attribute" value="ROC-1"/>
</operator>
<operator activated="true" class="series:sliding_window_validation" compatibility="5.0.2" expanded="true" height="112" name="Validation" width="90" x="313" y="30">
<process expanded="true" height="315" width="286">
<operator activated="true" class="adaboost" compatibility="5.0.11" expanded="true" height="76" name="AdaBoost" width="90" x="112" y="30">
<process expanded="true" height="297" width="603">
<operator activated="true" class="vote" compatibility="5.0.11" expanded="true" height="60" name="Vote" width="90" x="246" y="30">
<process expanded="true" height="331" width="714">
<portSpacing port="source_training set 1" spacing="0"/>
<portSpacing port="sink_base model 1" spacing="0"/>
</process>
</operator>
<connect from_port="training set" to_op="Vote" to_port="training set"/>
<connect from_op="Vote" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<connect from_port="training" to_op="AdaBoost" to_port="training set"/>
<connect from_op="AdaBoost" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="315" width="286">
<operator activated="true" class="apply_model" compatibility="5.0.11" expanded="true" height="76" name="Apply Model" width="90" x="63" y="31">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.0.11" expanded="true" height="76" name="Performance" width="90" x="112" y="165"/>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Windowing" to_port="example set input"/>
<connect from_op="Windowing" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="training" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
Any help greatly appreciated!
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Answers
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Hi inthewoods,
you missed to put some modeling operators into the Voting meta learner. Eg. you could but a Naaive Bayes, Decision Stump and a k-NN inside and let them vote.
Take a look into the RM help http://rapid-i.com/wiki/index.php?title=Vote
However, it seems to me a little bit odd to perform a AdaBoost on a Voting Meta Learner since the AdaBoost needs a lerner which can process attribute weights:
http://en.wikipedia.org/wiki/AdaBoost
I hope I could help,
Ciao Sebastian
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