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  1. 1
    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition

    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

    Published 2002
    “…Classification rules were generated from training feature vectors set, and a modified form of the standard voter classification algorithm, that use the rough sets generated rules, was applied. …”
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  2. 2
    Logistic regression methods for classification of imbalanced data sets

    Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

    Published 2012
    “…The imbalanced problem of both proposed general classification algorithms which is the limitation of accuracy performance specifically in classifying on the minority class has motivated this research to improve their classification performance on imbalanced data sets. …”
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  3. 3
    A framework of modified adaptive neuro-fuzzy inference engine

    A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

    Published 2012
    “…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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  4. 4
    A study on component-based technology for development of complex bioinformatics software

    A study on component-based technology for development of complex bioinformatics software by Ali Shah, Zuraini, Deris, Safaai, Othman, Muhamad Razib, Zakaria, Zalmiyah, Saad, Puteh, Hassan, Rohayanti, Muda, Mohd. Hilmi, Kasim, Shahreen, Roslan, Rosfuzah

    Published 2004
    “…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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  5. 5
    Generating type 2 trapezoidal fuzzy membership function using genetic tuning

    Generating type 2 trapezoidal fuzzy membership function using genetic tuning by Khairuddin, S.H., Hasan, M.H., Akhir, E.A.P., Hashmani, M.A.

    Published 2022
    “…The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. …”
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  6. 6
    Generating type 2 trapezoidal fuzzy membership function using genetic tuning

    Generating type 2 trapezoidal fuzzy membership function using genetic tuning by Khairuddin, S.H., Hasan, M.H., Akhir, E.A.P., Hashmani, M.A.

    Published 2022
    “…The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. …”
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  7. 7
    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

    Published 2013
    “…There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. …”
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  8. 8
    An adaptive ant colony optimization algorithm for rule-based classification

    An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

    Published 2020
    “…The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. …”
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  9. 9
    A new ant based rule extraction algorithm for web classification

    A new ant based rule extraction algorithm for web classification by Ku-Mahamud, Ku Ruhana, Saian, Rizauddin

    Published 2011
    “…Using Classifier-based attribute subset selection will reduce more attributes, but sacrifice the performance of the classifier.A hybrid ant colony optimization with simulated annealing algorithm to discover rules from data is proposed.The simulated annealing technique will minimize the problem of low quality discovered rule by an ant in a colony.The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set.The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set.The rule set is arranged in decreasing order of generation.Thirteen data sets which consist of discrete and continuous data were used to evaluate the performance of the proposed algorithm in terms of accuracy, number of rules and number of terms in the rules.Experimental results obtained from the proposed algorithm are comparable to the results of the Ant-Miner algorithm in terms of rule accuracy but are better in terms of rule simplicity.…”
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  10. 10
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Also, the proposed models for learning in data sets generated the classification rules faster than other methods. …”
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  11. 11
    Ant colony optimization algorithm for rule based classification: Issues and potential

    Ant colony optimization algorithm for rule based classification: Issues and potential by Al-Behadili, Hayder Naser Khraibet, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2018
    “…This paper presents a review of related work of ACO rule classification which emphasizes the types of ACO algorithms and issues. …”
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  12. 12
    Rule pruning techniques in the ant-miner classification algorithm and its variants: A review

    Rule pruning techniques in the ant-miner classification algorithm and its variants: A review by Al-Behadili, Hayder Naser Khraibet, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2018
    “…Rule-based classification is considered an important task of data classification.The ant-mining rule-based classification algorithm, inspired from the ant colony optimization algorithm, shows a comparable performance and outperforms in some application domains to the existing methods in the literature.One problem that often arises in any rule-based classification is the overfitting problem. …”
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  13. 13
    An enhancement of classification technique based on rough set theory for intrusion detection system application

    An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

    Published 2019
    “…Thus, to deal with huge dataset, data mining technique can be improved by introducing discretization algorithm to increase classification performance. …”
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  14. 14
    Ant colony optimization for rule induction with simulated annealing for terms selection

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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  15. 15
    DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH

    DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH by LUONG, TRUNG TUAN

    Published 2005
    “…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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    Final Year Project
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  16. 16
    Evaluation and optimization of frequent association rule based classification

    Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

    Published 2014
    “…Deriving useful and interesting rules from a data mining system is an essential and important task. …”
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  17. 17
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…Based on the experiment results, the classification method using the TIP approach has successfully performed rules generation and classification tasks as required during a classification operation. …”
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  18. 18
    Fuzzy modeling using Bat Algorithm optimization for classification

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. …”
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    Undergraduates Project Papers
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  19. 19
    Hybrid ant colony optimization and genetic algorithm for rule induction

    Hybrid ant colony optimization and genetic algorithm for rule induction by Al-Behadili, Hayder Naser Khraibet, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2020
    “…In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. …”
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  20. 20
    A hybrid-based modified adaptive fuzzy inference engine for pattern classification

    A hybrid-based modified adaptive fuzzy inference engine for pattern classification by Sayeed, Md. Shohel, Ramli, Abdul Rahman, Hossen, Md. Jakir, Samsudin, Khairulmizam, Rokhani, Fakhrul Zaman

    Published 2011
    “…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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