The emergence of ubiquitous sources of streaming data has given rise to the popularity of algorithms for online machine learning. In that context, Hoeffding trees represent the state-of-the-art algorithms for online classification. Their popularity stems in large part from their ability to process large quantities of data with a speed that goes beyond the processing power of any other streaming...

Customer churn is a serious problem, which critical issue encountered by large businesses and organizations. Due to the direct impact on company's revenues, particularly in sectors such as telecommunications well banking, companies are working promote ways identify of prospective consumers. Hence it vital investigate issues that influence customer yield appropriate measures diminish churn. The ...

advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. in this article, we proposed a method to predict the rate of student enrollment in coming years. the data for this research were from data sets of voluntee...

Aimed at the problem of huge computation, large tree size and over-fitting of the testing data for multivariate decision tree (MDT) algorithms, we proposed a novel roughset-based multivariate decision trees (RSMDT) method. In this paper, the positive region degree of condition attributes with respect to decision attributes in rough set theory is used for selecting attributes in multivariate tes...

Recursive partitioning is the core of several statistical methods including Classification and Regression Trees, Random Forest, and AdaBoost. Despite the popularity of tree based methods, to date, there did not exist methods for combining multiple trees into a single tree, or methods for systematically quantifying the discrepancy between two trees. Taking advantage of the recursive structure in...

Classification trees (J48) were induced to predict the habitat requirements of tench (Tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in Flanders (Belgium). The input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (pH, dissolved oxygen, water temperature and electric conduct...

We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. In case two leaf nodes with a common parent have the same class after relabeling, the tree is pruned in the parent node. Since we consider problems with ordered class labels, all results are evaluated on the basis of L1...

Regression trees are one of the oldest forms AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within large literature on regression trees, there has been little effort towards full provable optimization, mainly due to computational hardness problem. This work proposes dynamic programming-with-bounds ap...