doi: 10.4304/jsw.7.4.919-926
Data Dependant Learners Ensemble Pruning
2Computer Science Department, SUN YAT-SEN University, GuangZhou, China
Abstract—Ensemble learning aims at combining several slightly different learners to construct stronger learner. Ensemble of a well selected subset of learners would outperform than ensemble of all. However, the well studied accuracy / diversity ensemble pruning framework would lead to over fit of training data, which results a target learner of relatively low generalization ability. We propose to ensemble with base learners trained by both labeled and unlabeled data, by adopting data dependant kernel mapping, which has been proved successful in semisupervised learning, to get more generalized base learners. We bootstrap both training data and unlabeled data, namely point cloud, to build slight different data set, then construct data dependant kernel. With such kernels data point can be mapped to different feature space which results effective ensemble. We also proof that ensemble of learners trained by both labeled and unlabeled data is of better generalization ability in the meaning of graph Laplacian. Experiments on UCI data repository show the effectiveness of the proposed method.
Index Terms—ensemble learning; generalization ability; data dependant kernel; kernel mapping; point cloud kernel
Cite: Gang Zhang, Jian Yin2, Xiaomin He, and Lianglun Cheng, "Data Dependant Learners Ensemble Pruning," Journal of Software vol. 7, no. 4, pp. 919-926, 2012.
General Information
ISSN: 1796-217X (Online)
Abbreviated Title: J. Softw.
Frequency: Quarterly
APC: 500USD
DOI: 10.17706/JSW
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Cecilia Xie
Abstracting/ Indexing: DBLP, EBSCO,
CNKI, Google Scholar, ProQuest,
INSPEC(IET), ULRICH's Periodicals
Directory, WorldCat, etcE-mail: jsweditorialoffice@gmail.com
-
Oct 22, 2024 News!
Vol 19, No 3 has been published with online version [Click]
-
Jan 04, 2024 News!
JSW will adopt Article-by-Article Work Flow
-
Apr 01, 2024 News!
Vol 14, No 4- Vol 14, No 12 has been indexed by IET-(Inspec) [Click]
-
Apr 01, 2024 News!
Papers published in JSW Vol 18, No 1- Vol 18, No 6 have been indexed by DBLP [Click]
-
Jun 12, 2024 News!
Vol 19, No 2 has been published with online version [Click]