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 .

Table Of Contents

• Latest Version
• All Versions
• View Java Class Source Code in JAR file

Latest Version

Download nz.ac.waikato.cms.weka : supervisedAttributeScaling JAR file - Latest Versions:

• Latest Stable: 1.0.2.jar

All Versions

Download nz.ac.waikato.cms.weka : supervisedAttributeScaling JAR file - All Versions:

Version		Vulnerabilities	Size	Updated
1.0.x	supervisedAttributeScaling-1.0.2.jar		18.37 KB	Oct 30, 2018
	supervisedAttributeScaling-1.0.0.jar		18.42 KB	Jun 27, 2013

View Java Class Source Code in JAR file

1. Download JD-GUI to open JAR file and explore Java source code file (.class .java)
2. Click menu "File → Open File..." or just drag-and-drop the JAR file in the JD-GUI window supervisedAttributeScaling-1.0.2.jar file.
   Once you open a JAR file, all the java classes in the JAR file will be displayed.

weka.filters.supervised.attribute

├─ weka.filters.supervised.attribute.SupervisedAttributeScaler.class - [JAR]

weka.classifiers.functions

├─ weka.classifiers.functions.NonNegativeLogisticRegression.class - [JAR]