Abstract

This paper reports the results of experiments measuring the performance of very simple rules on the datasets commonly used in machine learning research. The specific kind of rules examined in this paper,called "1-rules", are rules that classify an object on the basis of a single attribute (i.e. theyare 1-leveldecision trees). Section 2 describes a system, called 1R, whose input is a set of training examples and whose output is a 1-rule. In an experimental comparison involving 16 commonly used datasets, 1R'srules are only afew percentage points less accurate, on most of the datasets, than the decision trees produced by C4 (Quinlan, 1986). Section 3 examines possible improvements to 1R's criterion for selecting rules. It defines an upper bound, called 1R*, on the accuracythat such improvements can produce. 1R* turns out to be very similar to the accuracyofC4's decision trees. This result has twoimplications. First, it indicates that simple modifications to 1R might produce a system competitive with C4, although more fundamental modifications are required in order to outperform C4. Second, this result suggests that it may be possible to use the performance of 1-rules to predict the performance of the more complexhypotheses produced by standard learning systems

Citations