SPLIT SELECTION METHODS FOR CLASSIFICATION TREES

By Wei-Yin Loh, Yu-Shan Shih. 1997

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “Full decision tree optimization is known to be an NP-complete problem (Laurent and Rivest, 1976), and heuristic greedy splitting and pruning procedures have been the major type of approach since the 1980s to grow decision trees (Breiman et al., 1984; Quinlan, 1993; Loh and Shih, 1997; Mehta et al., ”
Interpreting Models via Single Tree Approximation (2016, background): “There are multiple choices of deﬁning the information gain in the literature [Breiman et al., 1984, Loh and Shih, 1997].”
Mathematical optimization in classification and regression trees (2021, methodology): “This approach is refined in Deng and Runger (2013), by also including the importance scores of the predictor variables (Louppe et al. 2013; Strobl et al. 2008), obtained in a preprocessing step running a preliminary RF.”
Weighted Oblique Decision Trees (2019, methodology): “Loh and Shih (1997) presented a two-step QUEST tree by spliting each node with signiﬁcance tests.”
A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles (2017, background): “5 [14], cart [15], guide [16] and quest [17]).”
Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites (2020, methodology): “1984), Chi-squared automatic interaction detection (CHAID) (Kass 1980), C5 (Quinlan 1993), and quick, unbiased, efficient, and statistical tree (QUEST) (Loh and Shih 1997).”
Optimal classification trees (2017, methodology): “1993), linear discriminant analysis (Loh and Shih 1997; López-Chau et al. 2013), Householder transformations (Wickramarachchi et al.”
Unbiased Recursive Partitioning: A Conditional Inference Framework (2006, methodology): “Two performance distributions are said to be equivalent when the performance of the conditional inference trees compared to the performance of one competitor ( rpart , QUEST or GUIDE ) does not differ by an amount of more than 10%.”
Top-down induction of decision trees classifiers - a survey (2005, background): “Loh and Shih [28] suggest estimating the missing value based on other instances.”
Applied Predictive Modeling (2013, background): “Finally, these trees suffer from selection bias: predictors with a higher number of distinct values are favored over more granular predictors (Loh and Shih 1997; Carolin et al. 2007; Loh 2010).”
Optimal trees for prediction and prescription (2018, methodology): “Most other approaches for trees with hyperplane splits apply other methods recursively to generate the splits in the tree, such as logistic regression [116], support vector machines [9], linear discriminant analysis [82, 83], and Householder transformations [120].”
Recent advances in decision trees: an updated survey (2022, methodology): “Among DTs that employ LDA family methods, this paper highlights FACT (Loh and Vanichsetakul 1988), CRUISE (Kim and Loh 2001), QUEST (Loh and Shih 1997), and GUIDE (Loh 2009).”
Random intersection trees (2013, methodology): “A typical tree classiﬁer such as CART (Breiman et al., 1984) works by building a decision tree greedily from root node to the leaves; see also Loh and Shih (1997).”
Classification Trees With Unbiased Multiway Splits (2001, unknown): “multiway) splits. CART (Breiman et al., 1984) and QUEST ( Loh and Shih, 1997 ) are members of the first group.”
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks (2014, background): “The goal of DT is to create a model that predicts the value of a target class for an un-seen test instance based on several input features (Loh & Shih, 1997; Safavian & Landgrebe, 1991; Turney, 1995).”