JUCS - Journal of Universal Computer Science 11(8): 1353-1366, doi: 10.3217/jucs-011-08-1353

Learning Decision Trees from Dynamic Data Streams

‡ LIACC, FEP - University of Porto, Porto, Portugal§ LIACC - University of Porto, Porto, Portugal

Corresponding author: João Gama ( jgama@liacc.up.pt ) Citation:
Gama J, Medas P (2005) Learning Decision Trees from Dynamic Data Streams. JUCS - Journal of Universal Computer Science 11(8): 1353-1366. https://doi.org/10.3217/jucs-011-08-1353 |

Abstract

This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, which works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class problems the algorithm builds a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples. Naive-Bayes in inner nodes play two different roles. They can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect changes in the class-distribution of the examples that traverse the node. When a change in the class-distribution is detected, all the sub-tree rooted at that node will be pruned. The use of naiveBayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect changes in the distribution of the examples are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodology was tested with artificial and real-world data sets. The experimental results show a very good performance in comparison to a batch decision tree learner, and high capacity to detect drift in the distribution of the examples.

Keywords

data streams, incremental decision trees, concept drift