Substantial improvements in the set-covering projection classifier CHIRP (composite hypercubes on iterated random projections)
Leland Wilkinson, Anushka Anand, Tuan Nhon Dang · ACM Transactions on Knowledge Discovery from Data · 2012
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 ∞ 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.