Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining criteria for tree-based regression and classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Machine Learning
A simple regression based heuristic for learning model trees
Intelligent Data Analysis
Feature set decomposition for decision trees
Intelligent Data Analysis
Decision trees for mining data streams
Intelligent Data Analysis
A global optimal algorithm for class-dependent discretization of continuous data
Intelligent Data Analysis
Tree-structured smooth transition regression models
Computational Statistics & Data Analysis
Intelligent Data Analysis - Analysis of Symbolic and Spatial Data
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An algorithm named EDLRT (entropy-based dummy variable logistic regression tree) has been developed to handle decision tree processes. The main feature of EDLRT is constructing an entropy-based non-linear regression tree in the form of logistic formula. EDLRT comprises two key steps: the first step is to establish a decision tree by selecting the splitting variables with maximum mutual information; the second step is to convert the splitting points into dummy variables and fit them into a logistic regression model, and use genetic or Lasso algorithm to estimate the coefficients of parameters. The mathematical treatment of various types of variables for entropy evaluation and splitting point determination is illustrated. The advantage in using mutual information as a key criterion in splitting variable selection is elucidated. Step-by-step procedure of decision tree construction and dummy variable manipulation are illustrated by case study. EDLRT is very tolerant to missing values and it is also very effective for outlier detection. These advantages are demonstrated with case studies.