Workflows

CHART based Feature Weightage (1)

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Features can be assigned weightage through the decision tree model. In this regard, RapidMiner's Auto Model comes quite handy. Divide the original data into training and testing datasets before applying the workflow to it. 

Created: 2020-06-30 | Last updated: 2020-06-30

Credits: Imran Ali Syed

Gradient Boosting Trees based Feature Weig... (1)

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Features can be assigned weightage through the gradient boosting trees model. In this regard, RapidMiner's Auto Model comes quite handy. Divide the original data into training and testing datasets before applying the workflow to it. 

Created: 2020-06-30 | Last updated: 2020-06-30

Credits: Imran Ali Syed

Random Forest based Feature Weightage (1)

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Features can be assigned weightage through the random forest model. In this regard, RapidMiner's Auto Model comes quite handy. Divide the original data into training and testing datasets before applying the workflow to it.  

Created: 2020-06-30 | Last updated: 2020-06-30

Credits: Imran Ali Syed

Semantic clustering with k-Medoids and ALC... (1)

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 This workflow loads data from a configuration file for DL-Learner (http://dl-learner.org) and uses ALCN Semantic Kernel [1] to cluster those data with k-Medoids algorithm. [1] N. Fanizzi, C. d’Amato, F. Esposito. Learning with Kernels in Description Logics. ILP 2008  

Created: 2012-05-30 | Last updated: 2012-06-07

Clustering data from DBpedia using AHC (1)

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 This workflow uses Aglomerative Hierarchical Clustering algorithm to build hierarchy of clusters on data downloaded from DBpedia, the semantic version of Wikipedia. RDF data are downloaded from SPARQL endpoint and merged with DBpedia ontology by "Build Knowledge Base". Set of items to cluster is selected with "SPARQL Selector" and later they are clustered by "Agglomerative Hierarchical Clustering" with distance measure induced by the Bloehdorn Kernel [1]. ...

Created: 2012-05-30 | Last updated: 2012-06-07

Evaluating semantic kernel with k-NN class... (1)

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This workflow uses k-NN classifier to evaluate quality of EL++ Convolution Kernel [1]. As a dataset one of the examples from DL-Learner project [2] is used. After preparing knowledge base with "Build Knowledge Base", the item to item distance matrix is computed with "Calculate Gram/Distance Matrix". Such a matrix is then used as an input to 10-fold cross-validation with k-NN as an classifier and average result is delivered. [1] L. Józefowski, A. Lawrynowicz, J...

Created: 2012-05-30 | Last updated: 2012-06-07