KNIME® Active Learning

This repository is maintained by spaiceship@knime.com">KNIME Team spAIceship.

The KNIME Active Learning plugin comprises a set of KNIME nodes for
modular active learning and novelty detection in KNIME. Active learning
methods use feedback from the user to selectively sample training data.

Please note: KNIME - Active Learning is contained
in KNIME Labs.

Concept

KNIME Active Learning models the active learning process with the
Active Learn Loop. The management of the data takes place in the
Active Learn Loop Start, the labeling (assigning class labels to rows)
in the node end. The creation of the query for the oracle takes place
inside the loop.

Example

This example illustrates the active learning process with KNIME Active
Learning:

• It starts with the Active Learn Loop Start node and ends with one
  of the Active Learn Loop End nodes.
• Each unlabeled row is assigned a score in the Score module.
• In the Select module, one (or more) rows are selected
  for labeling.
• The selected rows are then assigned a class label in the Active
  Learn Loop End node.

Example Workflows

You can download the example workflows from the KNIME public example
server (002_DataMining/002009_ActiveLearning - see here how to
connect…)

Contained Nodes

Active Learn Loop

The “Active Learn Loop” nodes provide the framework for the active
learning process. Each active learning process starts with the Active
Learn Loop Start node and ends with one of the Active Learn Loop End
nodes:

• Active Learn Loop End: This node provides an interface for a
  human oracle to label the selected rows.
• Auto Active Learn Loop End: This node provides an automated
  oracle for fully labeled datasets. It can be used for verification
  and testing.

Scorer Nodes

Scorer nodes are nodes which calculate a score for each row that
describes its relevance for the active learning process. KNIME Active
Learning provides scorer nodes grouped in the following categories:

• Uncertainty: Nodes in this category calculate their score based
  on a class probability distribution which is a configurable output
  of many predictor nodes.
• Density: Nodes in this category calculate and update a score
  initially based on the density of the feature space.
• Novelty Detection: Nodes in this category calculate their score
  based on novelty detection methods, e.g. a Kernel Null
  Foley-Sammon Transformation.
• Combiner: Nodes in this category calculate aggregation scores
  out of the combination of scores calculated by other scorers.
• All in one: Nodes in this category provide scorers which package
  modular algorithms into a fixed package for increased performance.

Selector Node

The “Element Selector Node” selects the n elements with the highest
score.

Development Notes

You can find instructions on how to work with our code or develop extensions for
KNIME Analytics Platform in the knime-sdk-setup repository
on BitBucket
or GitHub.

Join the Community!

• KNIME Forum