TY - CHAP
T1 - Ensemble learning approaches
AU - Liu, Han
AU - Gegov, Alexander
AU - Cocea, Mihaela
PY - 2015/9/17
Y1 - 2015/9/17
N2 - As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail. These methods are also discussed comparatively with respects to their advantages and disadvantages.
AB - As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail. These methods are also discussed comparatively with respects to their advantages and disadvantages.
KW - ensemble learning approach
KW - equal voting
KW - random forest
KW - rule learning algorithm
KW - rule-based learning method
UR - http://www.scopus.com/inward/record.url?scp=85041483582&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23696-4_6
DO - 10.1007/978-3-319-23696-4_6
M3 - Chapter (peer-reviewed)
AN - SCOPUS:85041483582
SN - 9783319236957
SN - 9783319370279
T3 - Studies in Big Data
SP - 63
EP - 73
BT - Rule Based Systems for Big Data
PB - Springer
ER -