id: 05570606
dt: a
an: 05570606
au: Hall, Lawrence O.; Banfield, Robert E.; Bowyer, Kevin W.; Kegelmeyer,
    W.Philip
ti: Boosting lite ‒ handling larger datasets and slower base classifiers.
so: Haindl, Michal (ed.) et al., Multiple classifier systems. 7th international
    workshop, MCS 2007, Prague, Czech Republic, May 23‒25, 2007.
    Proceedings. Berlin: Springer (ISBN 978-3-540-72481-0/pbk). Lecture
    Notes in Computer Science 4472, 161-170 (2007).
py: 2007
pu: Berlin: Springer
la: EN
cc: 
ut: classifier ensembles; boosting; support vector machines; decision trees
ci: 
li: doi:10.1007/978-3-540-72523-7_17
ab: Summary: In this paper, we examine ensemble algorithms (Boosting Lite and
    Ivoting) that provide accuracy approximating a single classifier, but
    which require significantly fewer training examples. Such algorithms
    allow ensemble methods to operate on very large data sets or use very
    slow learning algorithms. Boosting Lite is compared with Ivoting,
    standard boosting, and building a single classifier. Comparisons are
    done on 11 data sets to which other approaches have been applied. We
    find that ensembles of support vector machines can attain higher
    accuracy with less data than ensembles of decision trees. We find that
    Ivoting may result in higher accuracy ensembles on some data sets,
    however Boosting Lite is generally able to indicate when boosting will
    increase overall accuracy.
rv: