Uncertain decision tree classifier for mobile context-aware computing

Abstract

Knowledge discovery from uncertain data is one of the major challenges in building modern artificial intelligence applications. One of the greatest achievements in this area was made with a usage of machine learning algorithms and probabilistic models. However, most of these methods do not work well in systems which require intelligibility, efficiency and which operate on data are not only uncertain but also infinite. This is the most common case in mobile contex-aware computing. In such systems data are delivered in streaming manner, requiring from the learning algorithms to adapt their models iteratively to changing environment. Furthermore, models should be understandable for the user allowing their instant reconfiguration. We argue that all of these requirements can be met with a usage of incremental decision tree learning algorithm with modified split criterion. Therefore, we present a simple and efficient method for building decision trees from infinite training sets with uncertain instances and class labels.