Abstract
AutoUniv (AU) is a tool for creating classification models and generating data sets from them. AU is freely available for use by the Machine Learning and Data Mining communities in developing and testing algorithms which learn to classify.
| Original language | English |
|---|---|
| Publication status | Published (in print/issue) - 15 Sept 2010 |
Bibliographical note
Reference text: [1] UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/ .[2] LED Display Domain Data Set.
http://archive.ics.uci.edu/ml/datasets/LED+Display+Domain .
[3] Hickey RJ (1996) Noise modelling and evaluating learning from examples, Artificial Intelligence 82 : 157–179.
[4] Black M and Hickey RJ (1999) Maintaining the performance of a learned classifier under concept drift, Intelligent Data Analysis 3 : 453-474.
[5] Hickey RJ (2003) Learning Rare Class Footprints: the REFLEX Algorithm, Proceedings of Proceedings of the Second International Workshop on Learning with Imbalanced Data Sets, International Conference on Machine Learning, Washington, D.C., 21-24 August 2003 :89-96.
Outputmediatype: Available for download on web
Keywords
- Classification models
- Machine Learning
- Data Mining
- artificial data generation
- algorithm evaluation.