TY - GEN
T1 - On dataset complexity for case base maintenance
AU - Cummins, Lisa
AU - Bridge, Derek
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84856870276
U2 - 10.1007/978-3-642-23291-6_6
DO - 10.1007/978-3-642-23291-6_6
M3 - Conference proceeding
AN - SCOPUS:84856870276
SN - 9783642232909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 61
BT - Case-Based Reasoning Research and Development - 19th International Conference on Case-Based Reasoning, ICCBR 2011, Proceedings
T2 - 19th International Conference on Case-Based Reasoning, ICCBR 2011
Y2 - 12 September 2011 through 15 September 2011
ER -