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  1. 1

    Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea by Doreen Ying Ying, Sim, Chee Siong, Teh, Ahmad Izuanuddin, Ismail

    Published 2017
    “…The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. …”
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  2. 2

    A frequent pattern mining algorithm based on FP-growth without generating tree by Tohid, Hossein, Ibrahim, Hamidah

    Published 2010
    “…It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately.For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution.In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well.Our algorithm works based on prime factorization, and is called Frequent Pattern- Prime Factorization (FPPF).…”
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  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
    “…This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. …”
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  4. 4

    A frequent pattern mining algorithm based on FP-growth without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

    Published 2010
    “…Our algorithm works based on prime factorization, and is called Frequent Pattern-Prime Factorization (FPPF).…”
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  5. 5

    Accuracy and performance analysis for classification algorithms based on biomedical datasets by Al-Hameli, Bassam Abdo, Alsewari, Abdulrahman A., Khubrani, Mousa, Fakhreldin, Mohammoud

    Published 2021
    “…Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. …”
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  6. 6
  7. 7

    A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2009
    “…There are two new properties introduced in this method; a novel tree structure called PC_Tree and PC_Miner algorithm. …”
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  8. 8

    A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization by Chern-Tong, H., Aziz, I.A.

    Published 2018
    “…To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm. …”
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  9. 9

    A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization by Chern-Tong, H., Aziz, I.A.

    Published 2018
    “…To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm. …”
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  10. 10

    Using unique-prime-factorization theorem to mine frequent patterns without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

    Published 2011
    “…Our algorithm works based on prime factorization and is called Prime Factor Miner (PFM). …”
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  11. 11

    Discovering decision algorithm of distance protective relay based on rough set theory and rule quality measure by Othman, Mohamad Lutfi

    Published 2011
    “…The discovered decision algorithm and association rule from the Rough-Set based data mining had been compared with and successfully validated by those discovered using the benchmarking Decision-Tree based data mining strategy. …”
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  12. 12

    Delineating mangrove forest zone using spectral reflectance by Abdul Whab @ Abdul Wahab,, Zulfa

    Published 2020
    “…The objectives of this study were to: (1) examine the variation of electromagnetic spectral reflectance on trees species and colonizing mangrove forest, and (2) demarcate the zonation of tree species in mangrove forests associated with anthropogenic activities. …”
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  13. 13

    Algorithm for the legal regulation of internet financial crime by Ambaras Khan, Hanna, Ab. Rahman, Suhaimi, Xinxin, Mao

    Published 2024
    “…Data processing for criminal acts on Internet finance platforms is crucial, with the utilization of random forest algorithms, including Decision tree and Bagging integration algorithms. …”
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  14. 14

    A corrosion prediction model for oil and gas pipeline using CMARPGA by Chern-Tong, H., Aziz, I.B.A.

    Published 2016
    “…The decision tree is said optimum in term of the genetic algorithm is used to examine the correlation between a group of association rules instead of using one single rule in predicting a case. …”
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  15. 15

    A Mininet emulation study for SDN fat tree data center sleep mode routing algorithms by Fawzi S., Din N.M.

    Published 2025
    “…In this work meta heuristic algorithm is incorporated at the SDN central controller in a fat tree-based data centre for bandwidth usage monitoring, sleep decisions and path selection using Mininet emulation. …”
    Article
  16. 16

    An enhanced feature selection technique for classification of group based holy Quran verses by Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The proposed FS technique is a combination of filter-based information gain (IG) and wrapper-based CFS algorithms. …”
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    An enhanced feature selection technique for classification of group-based holy quran verses by Oyekunle, Adeleke Abdullahi

    Published 2018
    “…The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. …”
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    Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach by Shirley, Rufus, Noor Azlinda, Ahmad, Zulkurnain, Abdul-Malek, Noradlina, Abdullah

    Published 2023
    “…Then the dataset is trained and tested with five Machine Learning (ML) algorithms, including Decision Trees (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). …”
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