Instance Selection and Prototype Based Rules Example 6

Created: 2011-11-05 22:26:12 Last updated: 2011-11-07 10:02:16

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Example of Instance optimization using LVQ algorithm for training kNN classifier (Requires Instance Selection and Prototype Based Rules):

Here we use FCM clustering to initialize LVQ network. The "Class assigner" is responsible for assigning class label for each cluster center, then obtained ExampleSet is used as codebooks initialization in the LVQ operator, which on the Prototype (Pro) output delivers the new optimized position of codebooks (prototypes) for training nearest neighbor classifier.

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Marcin Blachnik

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Instance Selection and Prototype Based Rules Example 6

Created by Marcin Blachnik on Saturday 05 November 2011 22:26:12 (UTC)
Instance Selection and Prototype Based Rules Example 6

Created by Marcin Blachnik on Monday 07 November 2011 10:00:33 (UTC)

Last edited by Marcin Blachnik on Monday 07 November 2011 10:02:18 (UTC)

Revision comment:

Small modification, FCM should have at least 3 clusters, but the number of cluster was set to 2, so LVQ has not enough codebooks to correctly classify the data

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