Margin adaptive risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penalty used in the CART pruning algorithm is valid under margin condition. It is also show...

Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penalty used in the CART pruning algorithm is valid under a margin condition. It i...

In this paper we challenge three of the underlying principles of CART, a well know approach to the construction of classification and regression trees. Our primary concern is with the penalization strategy employed to prune back an initial, overgrown tree. We reason, based on both intuitive and theoretical arguments, that the pruning rule for classification should be different from that used fo...

Regression analysis is commonly undertaken to identify the effects of each of these characteristics on income (or expenditure) per capita. Attention is needed to choose the independent variables carefully, to be sure that they are indeed exogenous. A number of more exotic techniques are now available for this purpose, including classification and regression tree (CART) models and multiple-adapt...

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...

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping relation with the optimal operation mode calculated by dynamic programming (DP). A Le...

Variable refrigerant flow (VRF) systems are easily subjected to performance degradation due to refrigerant leakage, mechanical failure or improper maintenance after years of operation. Ideal VRF systems should equip with fault detection and diagnosis (FDD) program to sustain its normal operation. This paper presents the fault diagnosis method for refrigerant charge faults of variable refrigeran...