Learning Morphological Data of Tomato Fruits
Three methods for attribute reduction in conjunction with Neural Networks, Naive Bayes, and k-Nearest Neighbor classifiers are investigated here when classifying a particularly challenging data set. The difficulty encountered with this data set is mainly due to the high dimensionality and to some inbalance between classes. As a result of this research, a subset of only 8 attributes (out of 34) is identified leading to a 92.7% classification accuracy. The confusion matrix analysis identifies class 7 as the one poorly learned across all combinations of attributes and classifiers. This information can be further used to upsample this underrepresented class or to investigate a classifier less sensitive to imbalance.
Thomas, Joshua C.; Lambert, Matthew; Snyder, Benn; Janning, Michael C.; Haning, Jacob; Hu, Yanglong; Ahmad, Mohammad; and Visa, Sofia, "Learning Morphological Data of Tomato Fruits" (2011). Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference 2011, , 133-137. Retrieved from http://openworks.wooster.edu/facpub/83
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