TY - GEN
T1 - Data Set Partitioning in Evolutionary Instance Selection
AU - Kordos, Mirosław
AU - Czepielik, Łukasz
AU - Blachnik, Marcin
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows, finding the optimal subset becomes more difficult, especially that running the optimization too long leads to over-fitting. A solution to that problem, which we evaluate in this work is to reduce the search space by clustering the dataset, run the instance selection algorithm for each cluster and combine the results. We also address the issue of properly processing the instances close to the cluster boundaries, as this is where the drop of accuracy can appear. The method is experimentally verified on several regression datasets with thousands of instances.
AB - Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows, finding the optimal subset becomes more difficult, especially that running the optimization too long leads to over-fitting. A solution to that problem, which we evaluate in this work is to reduce the search space by clustering the dataset, run the instance selection algorithm for each cluster and combine the results. We also address the issue of properly processing the instances close to the cluster boundaries, as this is where the drop of accuracy can appear. The method is experimentally verified on several regression datasets with thousands of instances.
UR - https://www.scopus.com/pages/publications/85057070100
U2 - 10.1007/978-3-030-03493-1_66
DO - 10.1007/978-3-030-03493-1_66
M3 - Conference contribution
AN - SCOPUS:85057070100
SN - 9783030034924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 631
EP - 641
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Novais, Paulo
A2 - Camacho, David
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer Verlag
T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Y2 - 21 November 2018 through 23 November 2018
ER -