Przemys?aw Kazienko, Edwin Lughofer, Bogdan Trawinski,
"Hybrid and ensemble techniques in soft computing: recent advances and emerging trends"
, in Soft Computing, Vol. 19, Nummer 12, Springer, Seite(n) 3353-3355, 2015
Original Titel:
Hybrid and ensemble techniques in soft computing: recent advances and emerging trends
Sprache des Titels:
Englisch
Original Kurzfassung:
The application of hybrid and ensemble methodologies in
the field of soft computing (SC) and machine learning (ML)
has become more visible and attractive. The relevance of
these methodologies is motivated by their power of being
able to express knowledge contained in data sets in multiple
ways, benefiting each of the other, i.e., exploiting their diversity,
thus increasing the performance of sole base models
in terms of model accuracy and generalization capability by
intelligent combination strategies, especially while dealing
with high-dimensional complex regression and classification
problems. Another main reason for their popularity is the
high complementary of its components. The integration of
the basic technologies into hybrid machine learning solutions
facilitates more intelligent search, enhanced optimization,
reasoning and hybridization methods that match various
domain knowledge with empirical data to solve advanced
and complex problems.
Forschungs-