Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison (1994)
| Venue: | In Proceedings of the AAAI-94 Workshop on Case-Based Reasoning |
| Citations: | 66 - 3 self |
BibTeX
@INPROCEEDINGS{Aha94featureselection,
author = {David Aha and Richard L. Bankert},
title = {Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison},
booktitle = {In Proceedings of the AAAI-94 Workshop on Case-Based Reasoning},
year = {1994},
pages = {106--112},
publisher = {AAAI Press}
}
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Abstract
Accurate weather prediction is crucial for many activities, including Naval operations. Researchers within the meteorological division of the Naval Research Laboratory have developed and fielded several expert systems for problems such as fog and turbulence forecasting, and tropical storm movement. They are currently developing an automated system for satellite image interpretation, part of which involves cloud classification. Their cloud classification database contains 204 high-level features, but contains only a few thousand instances. The predictive accuracy of classifiers can be improved on this task by employing a feature selection algorithm. We explain why non-parametric case-based classifiers are excellent choices for use in feature selection algorithms. We then describe a set of such algorithms that use case-based classifiers, empirically compare them, and introduce novel extensions of backward sequential selection that allows it to scale to this task. Several of the approache...
