Fuzzy Sets and Systems
Computational Statistics & Data Analysis - Data analysis and inference in nonstandard settings
A practical Bayesian framework for backpropagation networks
Neural Computation
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Modern Applied Statistics with S
Modern Applied Statistics with S
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A flexible coefficient smooth transition time series model
IEEE Transactions on Neural Networks
EDLRT: Entropy-based dummy variables logistic regression tree
Intelligent Data Analysis
Hi-index | 0.03 |
This paper introduces a tree-based model that combines aspects of classification and regression trees (CART) and smooth transition regression (STR). The model is called the 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 multiple regimes. Decisions about splits are entirely based on a sequence of Lagrange multiplier (LM) tests of hypotheses. An alternative specification strategy based on a 10-fold cross-validation is also discussed and a Monte Carlo experiment is carried out to evaluate the performance of the proposed methodology in comparison with standard techniques. The STR-tree model outperforms CART when the correct selection of the architecture of simulated trees is discussed. Furthermore, the LM test seems to be a promising alternative to 10-fold cross-validation. Function approximation is also analyzed. When put into proof with real and simulated data sets, the STR-tree model has a superior predictive ability than CART.