KNIME Base Nodes version 4.1.3.v202005112252 by KNIME AG, Zurich, Switzerland
This node oversamples the input data (i.e. adds artificial rows) to enrich the training data. The applied technique is called SMOTE (Synthetic Minority Over-sampling Technique) by Chawla et al.
Some supervised learning algorithms (such as decision trees and neural nets) require an equal class distribution to generalize well, i.e. to get good classification performance. In case of unbalanced input data, for instance there are only few objects of the "active" but many of the "inactive" class, this node adjusts the class distribution by adding artificial rows (in the example by adding rows for the "active" class).
The algorithm works roughly as follows: It creates synthetic rows by extrapolating between a real object of a given class (in the above example "active") and one of its nearest neighbors (of the same class). It then picks a point along the line between these two objects and determines the attributes (cell values) of the new object based on this randomly chosen point.
To use this node in KNIME, install KNIME Core from the following update site:
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to firstname.lastname@example.org, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.