This paper introduces a tree-based model that combines aspects of CART (Classification and Regression Trees) and STR (Smooth Transition Regression). The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure. The resulting model can be analyzed as a smooth transition regression with multi...

Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree (CART) to segment the large dataset constitutes the first step of this approach. The second step of this approach is to develop a suitable regression model for each segment. Since segment sizes are not very large, we ...

Sentiment classification is a special task of text classification whose objective is to classify a text according to the sentimental polarities of opinions it contains e.g., favorable or unfavorable, positive or negative. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. ...

Forest cover is of great interest to a variety of scientific and land management applications, many of which require not only information on forest categories, but also tree canopy density. In previous studies, large area tree canopy density had been estimated at spatial resolutions of 1km or coarser using coarse resolution satellite images. In this study, a strategy is developed for estimating...

We consider the problem of maintaining the data-structures of a partition-based regression procedure in a setting where the training data arrives sequentially over time. We prove that it is possible to maintain such a structure in time O (log n) at any time step nwhile achieving a nearly-optimal regression rate of Õ ( n−2/(2+d) ) in terms of the unknown metric dimension d. Finally we prove a ne...

In certain research studies development of a reliable decision rule, which can be used to classify new observations into some predefined categories, plays an important role. The existing traditional statistical methods are inappropriate to use in certain specific situations, or of limited utility, in addressing these types of classification problems. There are a number of reasons for these diff...

Decision tree study is a predictive modelling tool that used over many grounds. It constructed through an algorithmic technique divided the dataset in different methods created on varied conditions. Decisions trees are extreme dominant algorithms drop under set of supervised algorithms. However, Trees appearance modest and natural, there nothing identical near how algorithm drives nearby proced...

A debutanizer column is an integral part of any petroleum refinery. Online composition monitoring of debutanizer column outlet streams is highly desirable in order to maximize the production of liquefied petroleum gas. In this article, data-driven models for debutanizer column are developed for real-time composition monitoring. The dataset used has seven process variables as inputs and the outp...