Abstract
Two melanoma data sets, with and without an important attribute called TDS, were studied. The index TDS is a valuable diagnostic parameter used in the diagnosis of melanoma. Both data sets were partitioned into training data sets (250 cases) and testing (26 cases). Two classifiers were used: the well-known k-th Nearest Neighbor (KNN) algorithm and the algorithm LEM2, a part of the data mining system LERS. KNN algorithm is a typical statistical method based on voting among k training cases that are as close to the tested case as possible. On the other hand, LEM2 induces rules, and then classification is based on voting among all rules that match the case. Surprisingly, both methods yield similar results. However, the KNN algorithm may produce a smaller error rate, but LEM2 offers explanation of its results.
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Grzymala-Busse, J.W., Hippe, Z.S. (2001). Melanoma Prediction Using k-Nearest Neighbor and LEM2 Algorithms. In: Kłopotek, M.A., Michalewicz, M., Wierzchoń, S.T. (eds) Intelligent Information Systems 2001. Advances in Intelligent and Soft Computing, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1813-0_4
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DOI: https://doi.org/10.1007/978-3-7908-1813-0_4
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