Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes

Abstract

We propose a fast and efficient sampling strategy to build decision trees from a very large database, even when there are many continuous attributes which must be discretized at each step. Successive samples are used, one on each tree node. After a brief description of two fast sequential simple random sampling methods, we apply elements of statistical theory in order to determine the sample size that is sufficient at each step to obtain a decision tree as efficient as one built on the whole database. Applying the method to a simulated database (virtually infinite size), and to five usual benchmarks, confirms that when the database is large and contains many numerical attributes, our strategy of fast sampling on each node (with sample size about n = 300 or 500) speed up the mining process while maintaining the accuracy of the classifier.