Benschowe's Workflows

Is Parameter Optimization Useful (1)

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This process compares two 10x10fold cross-validations with linear SVM for classification. One use the heuristic parameter choice C=1/avg(K(x,x)) and the other uses an internal 5fold cross-validation to tune the parameter C. Afterwards both performance values are compared via ANOVA and T-test to check whether there is a significant difference between both performance values. The behaviour of the optimized 10x10 XV is similar to that of the experiment 10x10fold_XV_with_optimizedSVM_classificat...

Created: 2010-10-28 | Last updated: 2010-10-28

10x10fold_XV_with_optimizedSVM_classification (1)

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This process performs 10 round of 10fold cross-validation for classification with a linear SVM. In each traning-phase the parameters C of the linear SVM is tuned by a 5fold cross-validation. The repetions are useful for estimating the real performance on a small sample high variance data set, e.g. microarray data.

Created: 2010-10-28

Find best feature selection method. (1)

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This processes searches for the best method for feature selection and the best feature subset size. Inside a parameter loop the number of selected features and the feature selection methods are evaluated with a 10fold cross-validation. The Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext ) is required. BUGFIX: Please note that the plugin downloaded before 5th November contains a bug. Due to a bug in RM the TopK-operator sorted the features in the wrong direction. ...

Created: 2010-10-19 | Last updated: 2010-12-10

Recursive Feature Elimination with fully o... (1)

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The Process shows how you can use the RecursiveFeatureElimination-Operator of the Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext) to select relevant features from a highdimensional dataset.

Created: 2010-10-19 | Last updated: 2010-12-10