Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features
نویسندگان
چکیده
Using gray-scale texture features recently becomes 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 NPhard. To provide an efficient suboptimal solution, three genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminative 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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