2011 CHIRPANewClassifierbasedonCompo

• (Wilkinson et al., 2011) ⇒ Leland Wilkinson, Anushka Anand, and Dang Nhon Tuan. (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-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020418

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+CHIRP%3A+A+New+Classifier+based+on+Composite+Hypercubes+on+Iterated+Random+Projections
• http://dl.acm.org/citation.cfm?id=2020408.2020418&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; random projections; supervised classification

Abstract

We introduce a classifier based on the L-infinity 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.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 CHIRPANewClassifierbasedonCompo	Leland Wilkinson Anushka Anand Dang Nhon Tuan			CHIRP: A New Classifier based on Composite Hypercubes on Iterated Random Projections				10.1145/2020408.2020418		2011