Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features
Liu, Siyi and Cohen, Joseph Paul and Ding, Wei

geneticcrater.pdf580.96kB
Type: Paper
Tags: machine learning, crater detection, bayesian classifier, genetic algorithms

Bibtex:
@inproceedings{Cohen:2011:GEF:2188812.2188820,
 author = {Cohen, Joseph Paul and Liu, Siyi and Ding, Wei},
 title = {Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features},
 booktitle = {Proceedings of the 24th International Conference on Advances in Artificial Intelligence},
 series = {AI'11},
 year = {2011},
 isbn = {978-3-642-25831-2},
 location = {Perth, Australia},
 pages = {61--71},
 numpages = {11},
 url = {http://dx.doi.org/10.1007/978-3-642-25832-9_7},
 doi = {10.1007/978-3-642-25832-9_7},
 acmid = {2188820},
 publisher = {Springer-Verlag},
 address = {Berlin, Heidelberg},
 keywords = {bayesian classifier, crater detection, genetic algorithms, machine learning},
	abstract = {Using gray-scale texture features has recently become a new trend in supervised machine learning crater detection algorithms. To provide better classification of craters in planetary images, feature subset selection is used to reduce irrelevant and redundant features. Feature selection is known to be NP-hard. To provide an efficient suboptimal solution, three genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. A significant increase in the classification ability of a Bayesian classifier in crater detection using image texture features.}
}
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