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Decision Tree
This chapter first introduces the basic concept of the decision tree, then introduces feature selection, tree-generation and tree-pruning through ID3...
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Decision Tree
The decision tree is an important algorithm in machine learning. They mimic human thinking while making decisions and thus usually are easy to...
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Decision Tree
Decision tree is one of the simplest, yet popular, machine learning algorithms. It has a very long history of research and application, and has many...
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An improved decision tree algorithm based on boundary mixed attribute dependency
As an effective extension of rough set theory, the variable precision neighborhood rough set model has been applied to the attribute dependency-based...
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Clustering Via Decision Tree Construction
Clustering is an exploratory data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or...
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Evaluating trustworthiness of decision tree learning algorithms based on equivalence checking
Learning algorithms and their implementations are used as black-boxes to produce decision trees, e.g., for realizing critical classification tasks. A...
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A Better Decision Tree: The Max-Cut Decision Tree with Modified PCA Improves Accuracy and Running Time
Decision trees are a widely used method for classification, both alone and as the building blocks of multiple different ensemble learning methods....
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Adversarially Robust Decision Tree Relabeling
Decision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common...
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On the Decision Tree Complexity of Threshold Functions
In this paper we study decision tree models with various types of queries. For a given function it is usually not hard to determine the complexity in...
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Cost-Sensitive Decision Tree Induction on Dirty Data
As the rapid growth of data in our society, dirty data are increasingly common. In the process of cost-sensitive decision tree induction, dirty data...
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Differential Private (Random) Decision Tree Without Adding Noise
The decision tree is a typical algorithm in machine learning and has multiple expanded variations. However, regarding privacy, few in the variations...
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Big data decision tree for continuous-valued attributes based on unbalanced cut points
The decision tree is a widely used decision support model, which can quickly mine effective decision rules based on the dataset. The decision tree...
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Decision tree boosted varying coefficient models
Varying coefficient models are a flexible extension of generic parametric models whose coefficients are functions of a set of effect-modifying...
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Adapting video-based programming instruction: An empirical study using a decision tree learning model
The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These...
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Networked Industrial Control Device Asset Identification Method Based on Improved Decision Tree
Industrial control device asset identification is essential to the active defense and situational awareness system for industrial control network...
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Entropy-Based Logic Explanations of Differentiable Decision Tree
Explainable reinforcement learning has evolved rapidly over the years because transparency of the model’s decision-making process is crucial in some...
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Decision Tree Learning
Given a training set, Decision Trees (DTs) [Quinlan, 1986] are predictive models represented as trees where each vertex represents a feature, or...
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Cautious Decision-Making for Tree Ensembles
Cautious classifiers are designed to make indeterminate decisions when the uncertainty on the input data or the model output is too high, so as to...
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Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree
Billions of gadgets are already online, making the IoT an essential aspect of daily life. However, the interconnected nature of IoT devices also...
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Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making
Decomposing complex real-world tasks into simpler subtasks and devising a subtask execution plan is critical for humans to achieve effective...