logo supervisedAttributeScaling

Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .

homepage: weka.sourceforge.net/doc.packages/supervisedAttributeScaling
fresh index:
last release: 3 years ago, first release: 3 years ago
packaging: jar
get this artifact from: central
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