research-article

Substantial improvements in the set-covering projection classifier CHIRP (composite hypercubes on iterated random projections)

Authors:

Leland Wilkinson,

Tuan Nhon DangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 6, Issue 4

Article No.: 19, Pages 1 - 18

https://doi.org/10.1145/2382577.2382583

Published: 18 December 2012 Publication History

Abstract

In Wilkinson et al. [2011] we introduced a new set-covering random projection classifier that achieved average error lower than that of other classifiers in the Weka platform. This classifier was based on an L^∞ norm distance function and exploited an iterative sequence of three stages (projecting, binning, and covering) to deal with the curse of dimensionality, computational complexity, and nonlinear separability. We now present substantial changes that improve robustness and reduce training and testing time by almost an order of magnitude without jeopardizing CHIRP's outstanding error performance.

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Cited By

Pham B(2018)A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide SusceptibilityJournal of the Geological Society of India10.1007/s12594-018-0862-591:3(355-362)Online publication date: 21-Mar-2018
https://doi.org/10.1007/s12594-018-0862-5

Index Terms

Substantial improvements in the set-covering projection classifier CHIRP (composite hypercubes on iterated random projections)
1. Information systems
  1. Information systems applications

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 6, Issue 4

Special Issue on the Best of SIGKDD 2011

December 2012

141 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2382577

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 18 December 2012

Accepted: 01 October 2012

Revised: 01 October 2012

Received: 01 October 2011

Published in TKDD Volume 6, Issue 4

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Cited By

Pham B(2018)A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide SusceptibilityJournal of the Geological Society of India10.1007/s12594-018-0862-591:3(355-362)Online publication date: 21-Mar-2018
https://doi.org/10.1007/s12594-018-0862-5

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