Sample selection based on multiple incremental decision trees in BSP programming library

Author

Wang, Shuo ; Wang, Jianjian ; Wang, Yi ; Wang, Xuezheng

Author_Institution

Machine Learning Center, Hebei Univ., Baoding, China

Volume

2

fYear

2012

fDate

15-17 July 2012

Firstpage

810

Lastpage

815

Abstract

The sample selection is a key in the active learning, because it intends to select the best informative sample which has no label from the pool or online. And then the selected sample needs to be added into the training sets for updating the classifier. This paper proposed a new method based on multiple incremental decision trees algorithm to measure the ambiguity of the unlabeled samples for the selection. For accelerating the computing speed, the algorithm is developed in the BSP (Bulk Synchronous Parallel) Programming Library which is a computing model for parallel programming.

Keywords

decision trees; learning (artificial intelligence); parallel algorithms; parallel programming; software libraries; BSP programming library; active learning; ambiguity measure; bulk synchronous parallel programming library; computing model; computing speed; multiple incremental decision trees algorithm; sample selection; unlabeled samples; Abstracts; Acceleration; Cancer; Classification algorithms; Diabetes; Engines; Single photon emission computed tomography; Active learning; Ambiguity; BSPlib; Multiple incremental decision tree; Sample selection; Unlabeled samples;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6359327

Filename

6359327