International Journal of Computational Intelligence Systems

Volume 7, Issue sup2, July 2014, Pages 35 - 43

Adaptive generalized ensemble construction with feature selection and its application in recommendation

Authors
Jin Tian, Nan Feng
Corresponding Author
Jin Tian
Available Online July 2014.
DOI
https://doi.org/10.1080/18756891.2014.947111How to use a DOI?
Keywords
Ensemble learning, Feature selection, Coevolution, Recommendation
Abstract
This paper presents an adaptive generalized ensemble method with refined feature selection strategy and self-adjusted mechanism for ensemble size. The coevolutionary algorithm is introduced to optimize the ensemble and the feature weighting. There are two stages in the proposed method. In the coevolutionary stage, a component network corresponds to a subpopulation and the feature set is designed in another subpopulation. All subpopulations are coevolved simultaneously. Moreover, the study on the ensemble size is conducted in the structure refining stage. Finally, we apply the proposed approach to a recommendation task. Experimental results indicate that the proposed algorithm can achieve good classification performance, small feature subsets and compact ensemble structure.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 100
Pages
35 - 43
Publication Date
2014/07
ISSN
1875-6883
DOI
https://doi.org/10.1080/18756891.2014.947111How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Jin Tian
AU  - Nan Feng
PY  - 2014/07
DA  - 2014/07
TI  - Adaptive generalized ensemble construction with feature selection and its application in recommendation
JO  - International Journal of Computational Intelligence Systems
SP  - 35
EP  - 43
VL  - 7
IS  - sup2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.947111
DO  - https://doi.org/10.1080/18756891.2014.947111
ID  - Tian2014/07
ER  -