Decision trees: a recent overview

By S. Kotsiantis. 2011

1,429 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “Finally, based on the weight assigned to each object, a new subset of objects randomly from the training dataset is selected to induce a new decision tree [113].”
Optimal Generalized Decision Trees via Integer Programming (2016, background): “There has been a large amount of literature on DTs since the 1980’s (see the recent survey [6] and references therein).”
On Explaining Decision Trees (2020, neutral): “A non-exhaustivelist of references includes [43,15,54,55,59,19,33,3,12,66,50].”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “The major limitation of decision trees is their bias in over-training and inability to gener-alise due to the bias-variance problem [69].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, methodology): “DT algorithms involve two processes, namely induction and inference, aimed at building the model and then carrying out the classification [124].”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “DT in ML is mainly categorized as classification and regression [112].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “Researchers in [129] summarised the main points for sim-”