CMP: A Fast Decision Tree Classifier Using Multivariate Predictions

Most decision tree classifiers are designed to keep class histograms for single attributes, and to select a particular attribute for the next split using said histograms. In this paper, we propose a technique where, by keeping histograms on attribute pairs, we achieve (i) a significant speed-up over traditional classifiers based on single attribute splitting, and (ii) the ability of building classifiers that use linear combinations of values from non-categorical attribute pairs as split criterion. Indeed, by keeping two-dimensional histograms, CMP can often predict the best successive split, in addition to computing the current one; therefore, CMP is normally able to grow more than one level of a decision tree for each data scan. CMP's performance improvements are also due to techniques whereby non-categorical attributes are discretized without loss in classification accuracy; in fact, we introduce simple techniques, whereby classification errors caused by discretization at one s...

• Type: Conference
• Year: 2000
• Where: ICDE
• Authors: Haixun Wang, Carlo Zaniolo
• Keywords: Database, Decision Tree Classifiers, ICDE 2000, Non-categorical Attribute Pairs, Single Attribute Splitting