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"Creating SVM learning sets"

I think I initially put this message in the wrong category, so here it is again:
Hi,

I've been trying to apply SVM on a batch of textual documents in order to evaluate the performance of a model I developed as part of my thesis. First I used the 01_TextClassificationXVal.xml example found in the text plugin documentation. The XML of this example is brought here (I deleted some of the text processing operators - which are irrelevent to my question - in order to make it smaller):

<operator name="Root" class="Process" expanded="yes">
<description text="#ylt#h3#ygt#Optimizing vector creation for text classification#ylt#/h3#ygt##ylt#p#ygt#This experiments shows how to apply a cross validation to a classifier that learns to separate two sets of texts.#ylt#/p#ygt#"/>
<operator name="TextInput" class="TextInput" expanded="yes">
<parameter key="create_text_visualizer" value="true"/>
<list key="namespaces">
</list>
<parameter key="prune_below" value="3"/>
<list key="texts">
<parameter key="graphics" value="../data/newsgroup/graphics"/>
<parameter key="hardware" value="../data/newsgroup/hardware"/>
</list>
</operator>
<operator name="XValidation" class="XValidation" expanded="yes">
<parameter key="leave_one_out" value="true"/>
<operator name="LibSVMLearner" class="LibSVMLearner">
<list key="class_weights">
</list>
<parameter key="kernel_type" value="linear"/>
<parameter key="shrinking" value="false"/>
</operator>
<operator name="OperatorChain" class="OperatorChain" expanded="yes">
<operator name="ModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="BinominalClassificationPerformance" class="BinominalClassificationPerformance">
<parameter key="AUC" value="true"/>
<parameter key="f_measure" value="true"/>
</operator>
</operator>
</operator>
</operator>

The problem I have with his example is that the smallest learning set I can use is half of the entire dataset (if I set the value of the cross validation to 2). I would like to use a tenth of the dataset for this purpose, as it is quite large. Is there an operator that can do that for me?

Thanks in advance,
Gil

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land

Hi Gil,
whats about sample your data? If I got you right, you don't want to use all your examples for learning. Perhabs you could a sampling algorithm for discarding that portion of data?

Greetings,
Sebastian

Legacy User

Hi Land,

Thanks for answering so quicklly.

You are right - I want to use only a small part of my set for learning, a much smaller part than what is offered by cross-validation. However, I don't know how to apply a sampling algorithm for a TextInput operator. Will it be possible for you (or anyone else, for that matter) to post an example how do do this?

In an attemp to overcome this problem from a different direction, I wrote a java code that can go over all the documents of my dataset and randomly create subsets, which I intended to use as learning sets. I then wrote two simple experiments - one for creating a model based on the subsets I created, and another one that loads that model and applies it one the entore dataset.

In order to make sure these two experiments function properly, I used half the dataset as the learning set (I thought this way I could compare my results to those pruduced by a 2-fold cross validation). Sadly, the results I got were much poorer than those produced by the cross-validation experiment - and I can't understand why that is the case. The XML of the two experiments is posted below - if I made a mistake, please help me understand what it is.

If someone could help me solve even one of these two problems, I think it will be all I need.

Thanks in advance,
Gil

The Two experiments:
1) The learning phase - creating the SVM model:

<?xml version="1.0" encoding="windows-1252"?>
<process version="4.1">

<operator name="Root" class="Process" expanded="yes">
<operator name="TextInput" class="TextInput" expanded="no">
<parameter key="create_text_visualizer" value="true"/>
<list key="namespaces">
</list>
<parameter key="prune_below" value="3"/>
<list key="texts">
<parameter key="type1" value="D:\exp\type1_learnign_set"/>
<parameter key="type2" value="D:\exp\type2_learnign_set"/>
</list>
<operator name="StringTokenizer" class="StringTokenizer">
</operator>
<operator name="EnglishStopwordFilter" class="EnglishStopwordFilter">
</operator>
<operator name="TokenLengthFilter" class="TokenLengthFilter">
<parameter key="min_chars" value="3"/>
</operator>
<operator name="PorterStemmer" class="PorterStemmer">
</operator>
<operator name="TermNGramGenerator" class="TermNGramGenerator">
</operator>
</operator>
<operator name="LibSVMLearner" class="LibSVMLearner">
<list key="class_weights">
</list>
<parameter key="kernel_type" value="linear"/>
</operator>
<operator name="ModelWriter" class="ModelWriter">
<parameter key="model_file" value="C:\Documents and Settings\Admin\Desktop\SVM_Model.mod"/>
</operator>
</operator>

</process>

2) The test phase - applying the model

<?xml version="1.0" encoding="windows-1252"?>
<process version="4.1">

<operator name="Root" class="Process" expanded="yes">
<operator name="TextInput" class="TextInput" expanded="no">
<parameter key="create_text_visualizer" value="true"/>
<list key="namespaces">
</list>
<parameter key="prune_below" value="3"/>
<list key="texts">
<parameter key="type1" value="D:\exp\type1_full_set"/>
<parameter key="type2" value="D:\exp\type2_full_set"/>
</list>
<operator name="StringTokenizer" class="StringTokenizer">
</operator>
<operator name="EnglishStopwordFilter" class="EnglishStopwordFilter">
</operator>
<operator name="TokenLengthFilter" class="TokenLengthFilter">
<parameter key="min_chars" value="3"/>
</operator>
<operator name="PorterStemmer" class="PorterStemmer">
</operator>
<operator name="TermNGramGenerator" class="TermNGramGenerator">
</operator>
</operator>
<operator name="ModelLoader" class="ModelLoader">
<parameter key="model_file" value="C:\Documents and Settings\Admin\Desktop\SVM_Model.mod"/>
</operator>
<operator name="ModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="BinominalClassificationPerformance" class="BinominalClassificationPerformance">
<parameter key="AUC" value="true"/>
<parameter key="f_measure" value="true"/>
</operator>
</operator>

</process>

TobiasMalbrecht

Hi Gil,

well there is no direct and easy way to execute a cross validation but to use say only 10% of the examples for training and the other 90% for testing purposes. The easy-to-accomplish option you have is to simply use a sampling operator (e.g. [tt]StratifiedSampling[/tt]) before a cross validation. Therewith you may simply discard perhaps about 50% of your data and do a "normal" cross validation on the remaining 50%.

Otherwise you can nearly simulate a kind of multiple validation by the following process:


<operator name="Root" class="Process" expanded="yes">
    <operator name="NominalExampleSetGenerator" class="NominalExampleSetGenerator">
    </operator>
    <operator name="ParameterIteration" class="ParameterIteration" expanded="yes">
        <parameter key="keep_output"	value="true"/>
        <list key="parameters">
          <parameter key="SimpleValidation.local_random_seed"	value="1,2,3,4,5,6,7,8,9,10"/>
        </list>
        <operator name="SimpleValidation" class="SimpleValidation" expanded="yes">
            <parameter key="local_random_seed"	value="10"/>
            <operator name="NaiveBayes" class="NaiveBayes">
            </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>
    <operator name="AverageBuilder" class="AverageBuilder">
    </operator>
</operator>

Note, however, that the examples are not partitioned in the iterations.

Regards,
Tobias