Classification is an important data mining problem. Given a training database of records, each tagged with a class label, the goal of classification is to build a concise model that can be used to predict the class label of future, unlabeled records. A very popular class of classifiers are decision trees. All current algorithms to construct decision trees, including all main-memory algorithms, make one scan over the training database per level of the tree. We introduce a new algorithm (BOAT) for decision tree construction that improves upon earlier algorithms in both performance and functionality. BOAT constructs several levels of the tree in only two scans over the training database, resulting in an average performance gain of 300% over previous work. The key to this performance improvement is a novel optimistic approach to tree construction in which we construct an initial tree using a small subset of the data and refine it to arrive at the final tree. We guarantee that any difference with respect to the "real" tree (i.e., the tree that would be constructed by examining all the data in a traditional way) is detected and corrected. The correction step occasionally requires us to make additional scans over subsets of the data; typically, this situation rarely arises, and can be addressed with little added cost. Beyond offering faster tree construction, BOAT is the first scalable algorithm with the ability to incrementally update the tree with respect to both insertions and deletions over the dataset. This property is valuable in dynamic environments such as data warehouses, in which the training dataset changes over time. The BOAT update operation is much cheaper than completely rebuilding the tree, and the resulting tree is guaranteed to be identical to the tree that would be produced by a complete re-build.
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