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How to add weights to k-NN [SOLVED]

Hello,

I just sitting in a Rapid-I course I had the question how to add weights to the attributes for an k-NN operator. Now one could answer this satisfying.

So how to weight attributes (e.g.numeric ones) for a weighted distance for the k-NN operator?

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MariusHelf

Hey,

just let me know when your next break starts and I can explain it to you personally

See you later!

Marius

michaelhecht

Ok, meanwhile I understood what to do. One could normalize the attributes and then scale by weights.

But what can I do with nominal attributes?

MariusHelf

Well, for nominal attributes it is not possible to apply weights directly. You have to convert them to a numerical representation beforehand and then use the same technique as for numerical attributes, i.e. scaling the values. For the conversion you can e.g. use Nominal to Numerical with dummy_coding.

Best regards,
Marius

michaelhecht

I just discussed this with Ralf Klinkenberg. Here is what I implemented this night in the hotel


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.007">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.007" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="5.3.007" expanded="true" height="60" name="Retrieve Golf" width="90" x="45" y="30">
        <parameter key="repository_entry" value="//Samples/data/Golf"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="5.3.007" expanded="true" height="94" name="Normalize" width="90" x="179" y="30">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="|Humidity|Temperature"/>
      </operator>
      <operator activated="true" class="nominal_to_binominal" compatibility="5.3.007" expanded="true" height="94" name="Nominal to Binominal" width="90" x="313" y="30">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="|Wind|Play|Outlook"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.3.007" expanded="true" height="94" name="Nominal to Numerical" width="90" x="447" y="30">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="|Wind|Play|Outlook = sunny|Outlook = rain|Outlook = overcast"/>
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="5.3.007" expanded="true" height="94" name="Normalize (2)" width="90" x="581" y="30">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="|Wind = true|Wind = false|Outlook = sunny = true|Outlook = sunny = false|Outlook = rain = true|Outlook = rain = false|Outlook = overcast = true|Outlook = overcast = false"/>
      </operator>
      <operator activated="true" class="weight_by_information_gain_ratio" compatibility="5.3.007" expanded="true" height="76" name="Weight by Information Gain Ratio" width="90" x="45" y="210"/>
      <operator activated="true" class="scale_by_weights" compatibility="5.3.007" expanded="true" height="76" name="Scale by Weights" width="90" x="179" y="210"/>
      <operator activated="true" class="k_nn" compatibility="5.3.007" expanded="true" height="76" name="k-NN" width="90" x="313" y="210">
        <parameter key="k" value="2"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="5.3.007" expanded="true" height="76" name="Apply Model" width="90" x="447" y="210">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" compatibility="5.3.007" expanded="true" height="76" name="Performance" width="90" x="581" y="210"/>
      <connect from_op="Retrieve Golf" from_port="output" to_op="Normalize" to_port="example set input"/>
      <connect from_op="Normalize" from_port="example set output" to_op="Nominal to Binominal" to_port="example set input"/>
      <connect from_op="Nominal to Binominal" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Normalize (2)" to_port="example set input"/>
      <connect from_op="Normalize (2)" from_port="example set output" to_op="Weight by Information Gain Ratio" to_port="example set"/>
      <connect from_op="Weight by Information Gain Ratio" from_port="weights" to_op="Scale by Weights" to_port="weights"/>
      <connect from_op="Weight by Information Gain Ratio" from_port="example set" to_op="Scale by Weights" to_port="example set"/>
      <connect from_op="Scale by Weights" from_port="example set" to_op="k-NN" to_port="training set"/>
      <connect from_op="k-NN" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="k-NN" from_port="exampleSet" 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="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

This performs better than direct application of k-NN with two neighbours, i.e. seems to work as expected.

Nevertheless, what is missing (in my opinion) is a weight input for at least k-NN and Bayes-Operators (since weighting is e.g. a part of the Weka WAODE method, i.e. makes sense there and possibliy also for other operators) to apply attribute weighting in a "natural" way.

wessel

Marius wrote:

Hey,

just let me know when your next break starts and I can explain it to you personally

See you later!

Marius

Where do you guys hang out?
Dortmund University?

MariusHelf

michaelhecht wrote:

This performs better than direct application of k-NN with two neighbours, i.e. seems to work as expected.

Just for the record: to know if it really performs better you have to validate the model in a proper way, e.g. with a cross validation. Try setting k to 1 and you'll always get an accuracy of 100% on the training data

Where do you guys hang out?
Dortmund University?

Rapid-I Headquarters, Dortmund

michaelhecht

Well, I added SOLVED to the topic, but finally the difference to a weighted k-NN is, that the weights are taken squared if e.g. euclidean distance is applied. So it isn't a real solution but a workaround to the missing weight input.