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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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Effective Decision Tree Learning [PDF]
International Journal of Computer Applications, 2013 Classification is a data analysis technique. The decision tree is one of the most popular classification algorithms in current use for data mining because it is more interpretable. Training data sets are not error free due to measurement errors in the data collection process. Traditional decision tree classifiers are constructed without considering any
B. Kumara Swamy Achari +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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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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Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA [PDF]
IEEE Symposium on Field-Programmable Custom Computing Machines, 2019 Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage ...
Zhe Lin, Sharad Sinha, Wei Zhang
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On Learning and Testing Decision Tree
, 2021 In this paper, we study learning and testing decision tree of size and depth that are significantly smaller than the number of attributes $n$. Our main result addresses the problem of poly$(n,1/ )$ time algorithms with poly$(s,1/ )$ query complexity (independent of $n$) that distinguish between functions that are decision trees of size $s$ from ...
Bshouty, Nader H. +1 more
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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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Decision Tree Learning untuk Penentuan Jalur Kelulusan Mahasiswa [PDF]
, 2016 Jalur kelulusan merupakan hal penentu seorang mahasiswa untuk memperoleh gelar jenjang pendidikan strata satu pada sebuah Perguruan Tinggi. Penelitian ini bertujuan untuk mengetahui pemanfaatan algoritma ID3 dalam penentu jalur kelulusan serta ...
Ariestya, W. W. (Winda) +2 more
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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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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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