CHIRP: A new classifier based on composite hypercubes on iterated random projections

Leland Wilkinson, Anushka Anand, Dang Nhon Tuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

We introduce a classifier based on the L norm. This classifier, called CHIRP, is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability. CHIRP is not a hybrid or modification of existing classifiers; it employs a new covering algorithm. The accuracy of CHIRP on widely-used benchmark datasets exceeds the accuracy of competitors. Its computational complexity is sub-linear in number of instances and number of variables and subquadratic in number of classes.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages6-14
Number of pages9
DOIs
StatePublished - 2011
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: Aug 21 2011Aug 24 2011

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
CountryUnited States
CitySan Diego, CA
Period08/21/1108/24/11

Keywords

  • Random projections
  • Supervised classification

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    Wilkinson, L., Anand, A., & Tuan, D. N. (2011). CHIRP: A new classifier based on composite hypercubes on iterated random projections. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 (pp. 6-14). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2020408.2020418