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

Authors:
Leland Wilkinson;Anushka Anand;Dang Nhon Tuan
Affiliations:
University of Illinois at Chicago, Chicago, IL, USA;University of Illinois at Chicago, Chicago, IL, USA;University of Illinois at Chicago, Chicago, IL, USA
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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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.