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Local Novelty Scorer

KNIME Active Learning Plug-in version 4.2.0.v202006171506 by KNIME AG, Zurich, Switzerland

This node calculates a local KNFST-model based on the nearest neighbors in the kernel space for every single data point that is to be tested. The corresponding source code is a Java adaption of the Matlab code provided by Bodesheim et al. for their paper "Local Novelty Detection in Multi-class Recognition Problems".

Options

Training Columns
The columns which should be used for training (right now only numeric columns can be used for training)
Class Column
The column containing the class of the training samples
Kernel
Choose the Kernel that should be used
  • HIK - Histogram intersection kernel
  • EXPHIK - Exponential variant of the HIK
  • RBF - Radial basis function kernel (with parameter sigma)
  • Polynomial - Polynomial kernel (with parameters gamma, bias and power)
Number of Neighbors
Select the number of neighbors that should be used for training the local models.
Normalize Novelty Scores
Check to normalize the Novelty Scores by the minimum distance between target points.
Sort Training Table
Check to sort training table by class prior to calculation of the local models. Only use if training table is not already sorted by class.

Input Ports

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KNFST model
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Test Data

Output Ports

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Novelty Scores

Installation

To use this node in KNIME, install KNIME Active Learning from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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Developers

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