Abstract
A geometrically motivated classi er is presented and applied, with both training and testing stages, to 3 real datasets. Our results compared to those from 23 other classi ers have the least error. The algorithm is based on parallel coordinates and : ~ has worst-case computational complexity O(NjP j) in the number of variables N and dataset size jP j, ~ provides comprehensible and explicit rules, ~ does dimensionality selection { where the minimal set of original variables (not transformed new variables as in Principal Component Analysis) required to state the rule is found, and ~ orders these variables so as to optimize the clarity of separation between the designated set and its complement.
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