Weighted Majority Decision among Several Region Rules for Scientific Discovery

We consider the classification problem of how to predict the values of a categorical attribute of interest using the other numerical attributes in a given set of tuples. Decision by voting such as bagging and boosting attempts to enhance the existing classification techniques like decision trees by using a majority decision among them. However, a high accuracy ratio of prediction sometimes requires complicated predictors, and makes it hard to understand the simple laws affecting the values of the attribute of interest. We instead consider another approach of using of at most several fairly simple voters that can compete with complex prediction tools. We pursue this idea to handle numeric datasets and employ region splitting rules as relatively simple voters. The results of empirical tests show that the accuracy of decision by several voters is comparable to that of decision trees, and the computational cost is inexpensive.