Optimization Problem

Hi,

I would like to find the best classification model w.r.t. accuracy
for a given example set. To achieve best results, my idea is to evaluate
different supervised learners and optimize their parameters. In addition,
different feature selection algorithms should be applied to provide most
suitable input for the parameter optimization of each learner.

So, my idea is something like a nested model:
for each learner that should be evaluated
  for each example set determined by particular feature selection
        perform parameter optimization for given feature set and learner
return: model model with maximal accuracy

What do you think about this idea? Does it make sense to mix a feature
selection and a learner parameter optimization to find the most accurate
model, i.e. to first  Or whould you proceed differently in that case?
Are other approaches more common in practice?

In am of the opinion that the most accurate model can be only found
when different example sets are provided for the parameter optimization
to get a high number of combinations for the performance evaluation.
Correct me if I'm wrong. :-)

If my idea is OK, I would ask you to help me modelling this use case
in RapidMiner. It should be something like sample
05_Features/10_ForwardSelection.xml but not using just the NearestNeighbor
as learner but an parameter optimization problem like
07_Meta/01_ParameterOptimization.xml.

This is the code for the feature selection:

<operator name="Root" class="Process" expanded="yes">
    <description text="#ylt#p#ygt# Transformations of the attribute space may ease
learning in a way, that simple learning schemes may be able to learn complex
functions. This is the basic idea of the kernel trick. But even without kernel
based learning schemes the transformation of feature space may be necessary to
reach good learning results. #ylt#/p#ygt#  #ylt#p#ygt# RapidMiner offers several
different feature selection, construction, and extraction methods. This
selection experiment (the well known forward selection) uses an inner cross
validation for performance estimation. This building block serves as fitness
evaluation for all candidate feature sets. Since the performance of a certain
learning scheme is taken into account we refer to experiments of this type as
#yquot#wrapper approaches#yquot#.#ylt#/p#ygt#  #ylt#p#ygt#Additionally the
experiment log operator plots intermediate results. You can inspect them online
in the Results tab. Please refer to the visualization sample experiments or the
RapidMiner tutorial for further details.#ylt#/p#ygt#  #ylt#p#ygt# Try the
following: #ylt#ul#ygt# #ylt#li#ygt#Start the experiment and change to
#yquot#Result#yquot# view. There can be a plot selected. Plot the
#yquot#performance#yquot# against the #yquot#generation#yquot# of the feature
selection operator.#ylt#/li#ygt# #ylt#li#ygt#Select the feature selection
operator in the tree view. Change the search directory from forward (forward
selection) to backward (backward elimination). Restart the experiment. All
features will be selected.#ylt#/li#ygt# #ylt#li#ygt#Select the feature selection
operator. Right click to open the context menu and repace the operator by
another feature selection scheme (for example a genetic algorithm).#ylt#/li#ygt#
#ylt#li#ygt#Have a look at the list of the experiment log operator. Every time
it is applied it collects the specified data. Please refer to the RapidMiner
Tutorial for further explanations. After changing the feature selection operator
to the genetic algorithm approach, you have to specify the correct values.
#ylt#table#ygt##ylt#tr#ygt##ylt#td#ygt##ylt#icon#ygt#groups/24/visualization#ylt#/icon#ygt##ylt#/td#ygt##ylt#td#ygt##ylt#i#ygt#Use
the experiment log operator to log values
online.#ylt#/i#ygt##ylt#/td#ygt##ylt#/tr#ygt##ylt#/table#ygt# #ylt#/li#ygt#
#ylt#/ul#ygt# #ylt#/p#ygt#"/>
    <operator name="Input" class="ExampleSource">
        <parameter key="attributes"        value="../data/polynomial.aml"/>
    </operator>
    <operator name="FS" class="FeatureSelection" expanded="yes">
        <operator name="XValidation" class="XValidation" expanded="yes">
            <parameter key="sampling_type"        value="shuffled sampling"/>
            <operator name="NearestNeighbors" class="NearestNeighbors">
                <parameter key="k"        value="5"/>
            </operator>
            <operator name="ApplierChain" class="OperatorChain" expanded="yes">
                <operator name="Applier" class="ModelApplier">
                    <list key="application_parameters">
                    </list>
                </operator>
                <operator name="Performance" class="Performance">
                </operator>
            </operator>
        </operator>
        <operator name="ExpLog" 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>

but I don't get to replace the NearestNeighbor but an parameter
optimization problem. Could you help me?

Regards,
Martin