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

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jin Tian
AU  - Nan Feng
PY  - 2014
DA  - 2014/07/01
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  - Supplement 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.947111
DO  - 10.1080/18756891.2014.947111
ID  - Tian2014
ER  -