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  1. 1

    Hyper-Heuristic Evolutionary Approach for Constructing Decision Tree Classifiers by Kumar, Sunil, Ratnoo, Saroj, Vashishtha, Jyoti

    Published 2021
    “…Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. …”
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    Article
  2. 2

    Building customer churn prediction models in Indonesian telecommunication company using decision tree algorithm by Ramadhanti,, Darin, Larasati, Aisyah, Muid, Abdul, Mohamad, Effendi

    Published 2023
    “…The best decision tree model has parameters of criterion information gain with a minimal gain = 0.01 and a max depth = 6. …”
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    Conference or Workshop Item
  3. 3

    Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea by Sim, Doreen Ying Ying

    Published 2018
    “…There is a stepwise prediction improvement from the classical approach of Boosted Decision Trees to the developed Boosted Pruned-Decision Trees and then to Boosted Pruned-Association-Rule-Minded-Decision Trees. …”
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  4. 4

    A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier by Noormadinah Allias, Megat NorulAzmi Megat Mohamed Noor, Mohd. Nazri Ismail, Kim de Silva, (UniKL MIIT)

    Published 2014
    “…A performance of anti-spam filter not only depends on the number of features and types of classifier that are used, but it also depends on the other parameter settings. Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. …”
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    Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd by Nur Farahaina, Idris

    Published 2022
    “…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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  6. 6

    Decision tree method for fault causes classification based on RMS-DWT analysis in 275 kV transmission lines network by Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ungku Amirulddin Al Amin, Ungku Anisa, Ab Kadir, Mohd Zainal Abidin

    Published 2021
    “…The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree.…”
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    Article
  7. 7

    Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A... by Hamsani, Aqil Khairy, Mohd Bahrin, Ummu Fatihah, Wan Abdul Manan, Wan Dorishah

    Published 2023
    “…To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. The classification model is trained and tested using various split ratios, and the optimal depth for the DT is determined through parameter tuning to achieve the best accuracy. …”
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    Article
  8. 8

    Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

    Published 2023
    “…Correlation methods; Decision trees; Forecasting; Meteorology; Water management; Wind; Decision-tree algorithm; Design models; Evaporation rate; Hydrological models; Input parameter; Irrigation system design; Malaysia; Non-linear phenomenon; Pan evaporation; Waters resources; Evaporation…”
    Conference Paper
  9. 9

    Decision tree method for fault causes classification based on rms-dwt analysis in 275 kv transmission lines network by Asman S.H., Aziz N.F.A., Amirulddin U.A.U., Kadir M.Z.A.A.

    Published 2023
    “…The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree. � 2021 by the authors. …”
    Article
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    First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms by Azwa, Abdul Aziz, Fadhilah, Ahmad

    Published 2014
    “…From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. …”
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    Conference or Workshop Item
  12. 12

    Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting by Ardiansyah, Soleh, Mazlina, Abdul Majid, Jasni, Mohamad Zain

    Published 2012
    “…Artificial Neural Network (ANN) provides better performance of forecasting but it tends to get stuck in local minima and there is no optimal way to determine the best classifier on it. Meanwhile, Decision Tree (DT) is able to generate classifier in the form of a tree. …”
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    Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram by Faris Francis Singaram, Fareena

    Published 2021
    “…The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. …”
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    Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting by Soleh, Ardiansyah

    Published 2015
    “…Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. The main process in extracting rules from MLP using decision tree for currency exchange rate forecasting can be divided into two stages. …”
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    Knowledge of extraction from trained neural network by using decision tree by Soleh, Ardiansyah, Mazlina, Abdul Majid, Jasni, Mohamad Zain

    Published 2017
    “…Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. …”
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    Conference or Workshop Item
  20. 20

    An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting by Kassim N.M., Santhiran S., Alkahtani A.A., Islam M.A., Tiong S.K., Mohd Yusof M.Y., Amin N.

    Published 2024
    “…This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. …”
    Article