In this paper, the authors consider the problem of computing a response curve for binary cellular automata, that is, the curve describing the dependence of the density of ones after many iterations of the rule on the initial density of ones. The authors demonstrate how this problem could be approached using rule 130 as an example. For this rule, preimage sets of finite strings exhibit recogniza...

We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules – for instance found by Apriori – using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose th...

This paper addresses an effective learning method that enables us to directly optimize neural network classifier's discrimination performance at a desired local operating range by maximizing a partial area under a receiver operating characteristic (ROC) or domain-specific curve, which is difficult to achieve with classification accuracy or mean squared error (MSE)-based learning methods. The ef...