TY - GEN
T1 - On the performance of stacked generalization classifiers
AU - Ozay, Mete
AU - Vural, Fatos Tunay Yarman
PY - 2008/7/28
Y1 - 2008/7/28
N2 - Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique performs better than the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers under the architecture. This work shows that the success of the SG highly depends on how the individual classifiers share to learn the training set, rather than the performance of the individual classifiers. The experiments explore the learning mechanisms of SG to achieve the high performance. The relationship between the performance of the individual classifiers and that of SG is also investigated.
AB - Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique performs better than the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers under the architecture. This work shows that the success of the SG highly depends on how the individual classifiers share to learn the training set, rather than the performance of the individual classifiers. The experiments explore the learning mechanisms of SG to achieve the high performance. The relationship between the performance of the individual classifiers and that of SG is also investigated.
KW - Ensemble learning
KW - Parallel computing
KW - Pattern recognition
KW - Stacked generalization
UR - http://www.scopus.com/inward/record.url?scp=47749086720&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47749086720&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69812-8_44
DO - 10.1007/978-3-540-69812-8_44
M3 - Conference contribution
AN - SCOPUS:47749086720
SN - 3540698116
SN - 9783540698111
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 445
EP - 454
BT - Image Analysis and Recognition - 5th International Conference, ICIAR 2008, Proceedings
T2 - 5th International Conference on Image Analysis and Recognition, ICIAR 2008
Y2 - 25 June 2008 through 27 June 2008
ER -