On the Evaluation Criterions for the Active Learning Processes

08/02/2011

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In many data mining applications collection of sufficiently large datasets is the most time consuming and expensive. On the other hand, industrial methods of data collection create huge databases, and make difficult direct applications of the advanced machine learning algorithms. To address the above problems, we consider active learning (AL), which may be very efficient either for the experimental design or for the data filtering. In this paper we demonstrate using the online evaluation opportunity provided by the AL Challenge that quite competitive results may be produced using a small percentage of the available data. Also, we present several alternative criteria, which may be useful for the evaluation of the active learning processes. The author of this paper attended special presentation in Barcelona, where results of the WCCI 2010 AL Challenge were discussed.

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On the Evaluation Criterions for the Active Learning Processes

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On the Evaluation Criterions for the Active Learning Processes

Related Research

When does Active Learning Work?

Active Learning for Argument Mining: A Practical Approach

Batch Active Learning Using Determinantal Point Processes

Fase-AL – Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning

Towards Active Simulation Data Mining

ALFAA: Active Learning Fingerprint Based Anti-Aliasing for Correcting Developer Identity Errors in Version Control Data

ALPS: Active Learning via Perturbations