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Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non-linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those relations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that variable thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.