We present what is, to the best of our knowledge, the first analysis that uses dataset complexity measures to evaluate case base editing algorithms. We select three different complexity measures and use them to evaluate eight case base editing algorithms. While we might expect the complexity of a case base to decrease, or stay the same, and the classification accuracy to increase, or stay the same, after maintenance, we find many counter-examples. In particular, we find that the RENN noise reduction algorithm may be over-simplifying class boundaries. © 2011 Springer-Verlag.
CITATION STYLE
Cummins, L., & Bridge, D. (2011). On dataset complexity for case base maintenance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6880 LNAI, pp. 47–61). https://doi.org/10.1007/978-3-642-23291-6_6
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