TY  - JOUR
T1  - Local Ensembles vs. Global Ensembles
AU - , S.B. Kotsiantis AU - , D.N. Kanellopoulos 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 1
SP  - 80
EP  - 87
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.80.87
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.80.87
KW  - Machine learning
KW  -data mining
KW  -classification
KW  -regression
AB  - 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.
ER  -