Abstract
The goal of instance selection is to identify which instances
(examples, patterns) in a large dataset should be selected as representatives of
the entire dataset, without significant loss of information. When a machine
learning method is applied to the reduced dataset, the accuracy of the model
should not be significantly worse than if the same method were applied to the
entire dataset. The reducibility of any dataset, and hence the success of instance
selection methods, surely depends on the characteristics of the dataset. However
the relationship between data characteristics and the reducibility achieved by
instance selection methods has not been extensively tested. This chapter adopts
a meta-learning approach, via an empirical study of 112 classification datasets,
to explore the relationship between data characteristics and the success of a
nai've instance selection method. The approach can be readily extended to
explore how the data characteristics influence the performance of many more
sophisticated instance selection methods.
(examples, patterns) in a large dataset should be selected as representatives of
the entire dataset, without significant loss of information. When a machine
learning method is applied to the reduced dataset, the accuracy of the model
should not be significantly worse than if the same method were applied to the
entire dataset. The reducibility of any dataset, and hence the success of instance
selection methods, surely depends on the characteristics of the dataset. However
the relationship between data characteristics and the reducibility achieved by
instance selection methods has not been extensively tested. This chapter adopts
a meta-learning approach, via an empirical study of 112 classification datasets,
to explore the relationship between data characteristics and the success of a
nai've instance selection method. The approach can be readily extended to
explore how the data characteristics influence the performance of many more
sophisticated instance selection methods.
| Original language | English |
|---|---|
| Title of host publication | Meta-learning in computational intelligence |
| Editors | Jankowski Norbert, Włodzisław Duch, Gra̧bczewski Krzysztof |
| Place of Publication | Berlin |
| Publisher | Springer-Verlag London Ltd. |
| Chapter | 2 |
| Pages | 77-95 |
| Number of pages | 19 |
| ISBN (Electronic) | 9783642209802 |
| ISBN (Print) | 9783642209796 |
| Publication status | Published - 2011 |