International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Many data mining problems involve an investigation of relationships between features in heterogeneous datasets, where different learning algorithms can be more appropriate for different regions. We propose the locally application of ensembles’ techniques. This methodology identifies local regions having similar characteristics and then uses combining techniques to describe the relationship between the data characteristics and the target value. We performed a comparison of the locally application of the combining techniques with the globally application of the combining techniques, on standard benchmark datasets and the locally application of the ensembles gives more accurate results.
How to cite this article:
S.B. Kotsiantis and D.N. Kanellopoulos . Local Ensembles vs. Global Ensembles.
DOI: https://doi.org/10.36478/ijscomp.2007.80.87
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2007.80.87