Sparse grid classifiers as base learners for AdaBoost

Alexander Heinecke; Benjamin Peherstorfer; Dirk Pflüger; Zhongwen Song

doi:10.1109/HPCSim.2012.6266906

Sparse grid classifiers as base learners for AdaBoost

Alexander Heinecke, Benjamin Peherstorfer, Dirk Pflüger, Zhongwen Song

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We consider a classification method based on sparse grids which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. In order to obtain competitive results, such sparse grid classifiers are usually enhanced by locally refining the underlying regular sparse grid. However, in order to parallelize the corresponding adaptive algorithms a thorough knowledge of the hardware is necessary. Instead of improving the performance by refining the sparse grid, we construct a team of classifiers relying just on regular sparse grids and employ them as base learners within AdaBoost. Our examples with synthetic and real-world datasets show that we can achieve similar or better results than with locally refined sparse grids or libSVM, with respect to both runtime and accuracy.

Original language	English (US)
Title of host publication	Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012
Pages	161-166
Number of pages	6
DOIs	https://doi.org/10.1109/HPCSim.2012.6266906
State	Published - Oct 8 2012
Event	2012 10th Annual International Conference on High Performance Computing and Simulation, HPCS 2012 - Madrid, Spain Duration: Jul 2 2012 → Jul 6 2012

Publication series

Name	Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012

Other

Other	2012 10th Annual International Conference on High Performance Computing and Simulation, HPCS 2012
Country	Spain
City	Madrid
Period	7/2/12 → 7/6/12

Keywords

AdaBoost
classification
data mining
parallelization
shared memory platforms

ASJC Scopus subject areas

Modeling and Simulation

Access to Document

10.1109/HPCSim.2012.6266906

Fingerprint Dive into the research topics of 'Sparse grid classifiers as base learners for AdaBoost'. Together they form a unique fingerprint.

Cite this

Heinecke, A., Peherstorfer, B., Pflüger, D., & Song, Z. (2012). Sparse grid classifiers as base learners for AdaBoost. In Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012 (pp. 161-166). [6266906] (Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012). https://doi.org/10.1109/HPCSim.2012.6266906

Sparse grid classifiers as base learners for AdaBoost. / Heinecke, Alexander; Peherstorfer, Benjamin; Pflüger, Dirk; Song, Zhongwen.

Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012. 2012. p. 161-166 6266906 (Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Heinecke, A, Peherstorfer, B, Pflüger, D & Song, Z 2012, Sparse grid classifiers as base learners for AdaBoost. in Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012., 6266906, Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012, pp. 161-166, 2012 10th Annual International Conference on High Performance Computing and Simulation, HPCS 2012, Madrid, Spain, 7/2/12. https://doi.org/10.1109/HPCSim.2012.6266906

Heinecke A, Peherstorfer B, Pflüger D, Song Z. Sparse grid classifiers as base learners for AdaBoost. In Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012. 2012. p. 161-166. 6266906. (Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012). https://doi.org/10.1109/HPCSim.2012.6266906

@inproceedings{f8bfb916acd645e19a8897c49236fcac,

title = "Sparse grid classifiers as base learners for AdaBoost",

abstract = "We consider a classification method based on sparse grids which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. In order to obtain competitive results, such sparse grid classifiers are usually enhanced by locally refining the underlying regular sparse grid. However, in order to parallelize the corresponding adaptive algorithms a thorough knowledge of the hardware is necessary. Instead of improving the performance by refining the sparse grid, we construct a team of classifiers relying just on regular sparse grids and employ them as base learners within AdaBoost. Our examples with synthetic and real-world datasets show that we can achieve similar or better results than with locally refined sparse grids or libSVM, with respect to both runtime and accuracy.",

keywords = "AdaBoost, classification, data mining, parallelization, shared memory platforms",

author = "Alexander Heinecke and Benjamin Peherstorfer and Dirk Pfl{\"u}ger and Zhongwen Song",

year = "2012",

month = oct,

day = "8",

doi = "10.1109/HPCSim.2012.6266906",

language = "English (US)",

isbn = "9781467323598",

series = "Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012",

pages = "161--166",

booktitle = "Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012",

note = "2012 10th Annual International Conference on High Performance Computing and Simulation, HPCS 2012 ; Conference date: 02-07-2012 Through 06-07-2012",

}

TY - GEN

T1 - Sparse grid classifiers as base learners for AdaBoost

AU - Heinecke, Alexander

AU - Peherstorfer, Benjamin

AU - Pflüger, Dirk

AU - Song, Zhongwen

PY - 2012/10/8

Y1 - 2012/10/8

N2 - We consider a classification method based on sparse grids which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. In order to obtain competitive results, such sparse grid classifiers are usually enhanced by locally refining the underlying regular sparse grid. However, in order to parallelize the corresponding adaptive algorithms a thorough knowledge of the hardware is necessary. Instead of improving the performance by refining the sparse grid, we construct a team of classifiers relying just on regular sparse grids and employ them as base learners within AdaBoost. Our examples with synthetic and real-world datasets show that we can achieve similar or better results than with locally refined sparse grids or libSVM, with respect to both runtime and accuracy.

AB - We consider a classification method based on sparse grids which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. In order to obtain competitive results, such sparse grid classifiers are usually enhanced by locally refining the underlying regular sparse grid. However, in order to parallelize the corresponding adaptive algorithms a thorough knowledge of the hardware is necessary. Instead of improving the performance by refining the sparse grid, we construct a team of classifiers relying just on regular sparse grids and employ them as base learners within AdaBoost. Our examples with synthetic and real-world datasets show that we can achieve similar or better results than with locally refined sparse grids or libSVM, with respect to both runtime and accuracy.

KW - AdaBoost

KW - classification

KW - data mining

KW - parallelization

KW - shared memory platforms

UR - http://www.scopus.com/inward/record.url?scp=84866978099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866978099&partnerID=8YFLogxK

U2 - 10.1109/HPCSim.2012.6266906

DO - 10.1109/HPCSim.2012.6266906

M3 - Conference contribution

AN - SCOPUS:84866978099

SN - 9781467323598

T3 - Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012

SP - 161

EP - 166

BT - Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012

T2 - 2012 10th Annual International Conference on High Performance Computing and Simulation, HPCS 2012

Y2 - 2 July 2012 through 6 July 2012

ER -