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

Authors:
Leland Wilkinson;Anushka Anand;Tuan Nhon Dang
Affiliations:
University of Illinois at Chicago, Chicago, IL;University of Illinois at Chicago, Chicago, IL;University of Illinois at Chicago, Chicago, IL
Venue:
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Year:
2012

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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&infty; 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.