Workflows

Instance Selection and Prototype Based Rul... (1)

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Example of Instance selection chain (Requires Instance Selection and Prototype Based Rules plugin): Beafore training kNN we do instance selection based on cascade of instance selection algorithms. Here ENN algorithm is followed by CNN algorithm. Instance selection algorithms form PRules plugin works as View on original ExampleSet

Created: 2011-11-05

Instance Selection and Prototype Based Rul... (1)

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Example of Instance selection: (Requires Instance Selection and Prototype Based Rules plugin): Here instance selection is based on All-kNN algorithm. Because the Pro output of any instance selection operator is just an ExampleSet object, so in fact any Learner can be used instead of kNN, like DecisionTree, but in this example the Decision Tree is based on the reduced ExampleSet .

Created: 2011-11-05

Instance Selection and Prototype Based Rul... (2)

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Example of Instance optimization (Requires Instance Selection and Prototype Based Rules): Here we use FCM (Fuzzy C-means) clustering to initialize kNN classifier. The "Class assigner" is responsible for assigning class label for each cluster center. The class assigner operator use Voronoi diagram and the majority voting for determining class label.

Created: 2011-11-05 | Last updated: 2011-11-07

Instance Selection and Prototype Based Rul... (1)

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Example of Instance optimization - using clustering for training kNN classifier (Requires Instance Selection and Prototype Based Rules) : Here we use FCM (Fuzzy c-means) clustering to initialize kNN classifier. Moreover centers of clusters are determined independent for each class. "Class iterator" operator iterates over each class label, and embedded clustering algorithm cluster the examples for each class independent. The Prototype (Pro) output of "Class iterator" delivers the concatena...

Created: 2011-11-05 | Last updated: 2011-11-05

Instance Selection and Prototype Based Rul... (2)

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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 n...

Created: 2011-11-05 | Last updated: 2011-11-07

Series of Factorials (1)

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 The workflow generates the series of factorial numbers.

Created: 2011-11-09 | Last updated: 2011-11-11

Handling data and time example (1)

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This process shows how date and time formats can be converted from arbitrary formats to other arbitrary formats with the operators "Nominal to Date" and "Date to Nominal".

Created: 2011-11-28

Parameter optimization (1)

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This is a parameter optimization workflow for rating prediction recommendation operators.

Created: 2012-02-10 | Last updated: 2012-02-10

Credits: Matej Mihelčić

De-normalizing Dataset After K-means (1)

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K-means is performed on the iris dataset. After finding the clusters, the dataset is de-normalized and the means of the different variables are calculated by cluster.

Created: 2011-10-19 | Last updated: 2011-10-19

POSTing CSV file to RapidAnalytics Web ser... (1)

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This is the first demo used in the RapidAnalytics video on creating Web services. It downloads three data sets provided by data.gov.uk and generates a regression model and stores it as "RegressionTree" in the repository.

Created: 2011-11-02