Decision tree methods: applications for classification and prediction

By Yanyan Song, Ying Lu. 2015

2,245 citations recorded by the source corpus.

Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Subfields: Computer Networks and Communications, Information Systems, Statistics and Probability

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Citations in the corpus

Classification Based on Decision Tree Algorithm for Machine Learning (2021, methodology): “Table I shown comparison between the frequently used algorithms for the decision tree [51].”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure (Song and Ying, 2015; Quinlan, 1987).”
A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges (2020, methodology): “In order to derive optimal feature set, two techniques are used namely information gain and gini index [70].”
Detection (2020, neutral): “In order to solve the supervised binary classification problem, we resort to multiple ML classifiers: k-Nearest-Neighbors (kNN) [26], Gaussian Naive Bayes (GNB) [27], decision tree (DT) [28], random forest (RF) [29], and eX-treme Gradient Boosting (XGB) [30].”
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, background): “Additional details about the DT model can be found in Quinlan (1986) and Song and Ying (2015).”