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

Leland Wilkinson; Anushka Anand; Dang Nhon Tuan

doi:10.1145/2020408.2020418

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

Leland Wilkinson, Anushka Anand, Dang Nhon Tuan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages	6-14
Number of pages	9
DOIs	https://doi.org/10.1145/2020408.2020418
State	Published - 2011
Event	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States Duration: Aug 21 2011 → Aug 24 2011

Publication series

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

Conference

Conference	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Country	United States
City	San Diego, CA
Period	08/21/11 → 08/24/11

Keywords

Random projections
Supervised classification

Access to Document

10.1145/2020408.2020418

Link to publication in Scopus

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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

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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.",

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Wilkinson, L, Anand, A & Tuan, DN 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. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 6-14, 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11, San Diego, CA, United States, 08/21/11. https://doi.org/10.1145/2020408.2020418

CHIRP : A new classifier based on composite hypercubes on iterated random projections. / Wilkinson, Leland; Anand, Anushka; Tuan, Dang Nhon.

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11. 2011. p. 6-14 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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

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Wilkinson L, Anand A, Tuan DN. 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. 2011. p. 6-14. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2020408.2020418