Classification tree analysis using TARGET

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “, 2020), (2) stochastic search through the space of trees (e.g., Yang et al., 2017; Gray and Fan, 2008; Janikow and Malatkar, 2011), and (3) customized dynamic programming algorithms that incorporate branch-and-bound techniques for reducing the size of the search space (Hu et al.”
Mining data with random forests: A survey and results of new tests (2011, background): “[58] G. Zhang, H. Li, B. Fang, Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition, Process Biochemistry 44 (2009) 654–660.”
Fifty Years of Classification and Regression Trees (2014, methodology): “Fan & Gray (2005) and Gray & Fan (2008) used a genetic algorithm for tree construction.”
Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping (2012, methodology): “Several methods have been developed to improve CART decision trees to avoid over fitting in searching for splitting rules (Gray and Fan, 2008).”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “, [17], [46], [47], [100], [102], [103], [105], and [111]).”