Application of Decision Tree-Based Classification Algorithm on Content Marketing
Journal of Mathematics, 2022 Traditional content marketing methods resort grossly to market requirements but barely obtain relatively accurate marketing prediction under loads of requirements.
Yi Liu, Shuo Yang
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Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 Educational Data Mining (EDM) often appears to be applied in big data processing in the education sector. One of the educational data that can be further processed with EDM is activity log data from an e-learning system used in teaching and learning ...
Safira Nuri Safitri +2 more
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The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm
Complexity, 2021 With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets.
Yongxian Yang
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Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree
Entropy, 2019 Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning.
Huaining Sun, Xuegang Hu, Yuhong Zhang
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Robust algorithm to learn rules for classification: A fault diagnosis case study [PDF]
FME Transactions, 2023 Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning.
Balaji Arun P., Sugumaran V.
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Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm
Discrete Dynamics in Nature and Society, 2022 With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology.
Fen Yang
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Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations [PDF]
, 2019 Consider the following heuristic for building a decision tree for a function $f : \{0,1\}^n \to \{\pm 1\}$. Place the most influential variable $x_i$ of $f$ at the root, and recurse on the subfunctions $f_{x_i=0}$ and $f_{x_i=1}$ on the left and right ...
Blanc, Guy, Lange, Jane, Tan, Li-Yang
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Multiple Instance Learning with Trainable Soft Decision Tree Ensembles
Algorithms, 2023 A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed.
Andrei Konstantinov +2 more
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METHOD OF BUILDING THE SEMANTIC NETWORK OF DISTRIBUTED SEARCH IN E-LEARNING
Сучасний стан наукових досліджень та технологій в промисловості, 2017 The subject matter of the article is semantic networks of distributed search in e-learning. The goal is to synthesize a decision tree and a stratified semantic network that allows network intelligent agents in the e-learning to construct inference ...
Nina Kuchuk +2 more
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Comprehensive decision tree models in bioinformatics.
PLoS ONE, 2012 PurposeClassification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of
Gregor Stiglic +3 more
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