difference aspects of multinomial statistical modelings and its classifications has been studied so far. in these type of problems y is the qualitative random variable with t possible states which are considered as classifications. the goal is prediction of y based on a random vector x ? ir^m. many methods for analyzing these problems were considered. one of the modern and general method of cla...

in the present study, classification trees (cts-j48 algorithm) were used to study the occurrence of roach in rivers in flanders (belgium). the presence/absence of roach was modelled based on a set of river characteristics. the predictive performance of the cts models was assessed based on the percentage of correctly classified instances (cci) and cohen's kappa statistics. to find the best model...

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics Machine Learning. CARTs traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) associated threshold. This approach trains trees very fast, but, its nature, their classification accuracy may not be competitive against other state-of-the-art procedur...

We propose to extend CART for bivariate marked point processes provide a segmentation of the space into homogeneous areas interaction between marks. While usual tree considers marginal distribution response variable at each node, proposed algorithm, SpatCART, takes account spatial location observations in splitting criterion. introduce dissimilarity index based on Ripley’s intertype K-function ...

We give characterizations for the parabolicity of regular trees.

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...

PURPOSE Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classificati...