The efficiency of deep learning and tree-based machine approaches has gained immense popularity in various fields. One model viz. convolution neural network (CNN), artificial (ANN) four models, namely, alternative decision tree (ADTree), classification regression (CART), functional logistic (LMT), were used for landslide susceptibility mapping the East Sikkim Himalaya region India, results comp...

A tree-structured classifier is a decision tree for predicting a class variable from one or more predictor variables. THAID [15, 7] was the first such algorithm. This article focuses on the CART R © [2], C4.5 [17], and GUIDE [12] methods. The algorithms are briefly reviewed and their similarities and differences compared on a real data set and by simulation. In a typical classification problem,...

A tree-structured classifier is a decision tree for predicting a class variable from one or more predictor variables. THAID [15, 7] was the first such algorithm. This article focuses on the CART R © [2], C4.5 [17], and GUIDE [12] methods. The algorithms are briefly reviewed and their similarities and differences compared on a real data set and by simulation. In a typical classification problem,...

Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn this data into rules. Previous research has focused on how to obtain list of these associations and use these “frequent item sets” for prediction purpose. This paper proposes a technique which uses partial information about the contents of the shopping carts for the predicti...

CART was introduced by Breiman et al. (1984) as a classification tool. It divides the whole sample recursively in two subpopulations by finding the best possible split with respect to a optimisation criterion. This method, restricted up to date to binary splits, is extended in this paper for allowing also multiple splits. The main problem with this extension is related to the optimal number of ...

Undirected graphical models encode in a graph G the dependency structure of a random vector Y . In many applications, it is of interest to model Y given another random vector X as input. We refer to the problem of estimating the graph G(x) of Y conditioned on X = x as “graph-valued regression”. In this paper, we propose a semiparametric method for estimating G(x) that builds a tree on the X spa...

Abstract Decision trees have favorable properties, including interpretability, high computational efficiency, and the ability to learn from little training data. Learning a decision tree is known be NP-complete. The researchers proposed many greedy algorithms such as CART approximate solutions. Inspired by current popular neural networks, soft that support end-to-end with back-propagation attra...

The aim of this study was to develop and validate a new method: a classification and regression tree (CART) based on easily accessible measures to predict mortality in patients with stable chronic obstructive pulmonary disease (COPD). This was a prospective study of two independent prospective cohorts: a derivation cohort with 611 recruited patients and a validation cohort with 348 patients, al...

The increasing amount and complexity of today's data available in science, business, industry and many other areas creates an urgent need to accelerate discovery of knowledge in large databases . Such data can provide a rich resource for knowledge discovery and decision support. To understand, analyze and eventuall y use this data, a multidisciplinary approach called data mining has been propos...

background and objectives: arteriovenous fistula is a popular vascular access method for surgical treatmentof hemodialysis patients. the method, however, is associated with a high rate of early failure varying in the range of 20-60%. predicting early arteriovenous fistula failure and its risk factors can help reduce its incidence, its hospitalization rate, and associated costs. in this study, w...