Search Results - (( new evaluation new algorithm ) OR ( data classification based algorithm ))*

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  1. 1
    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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  2. 2
    An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani

    An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani by Ab Ghani, Nur Laila

    Published 2015
    “…The urban growth images obtained are analysed to improve existing classification algorithms. The improved algorithm is constructed by adding new parameter and classification rule to existing algorithm. …”
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    Thesis
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  3. 3
    Case Slicing Technique for Feature Selection

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…Since the 1960s, many algorithms for data classification have been proposed. …”
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  4. 4
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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  5. 5
    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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  6. 6
    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
    “…Methods to reduce the number of attributes and discretization are two important data pre-processing steps before the data can be used for classification activity. …”
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    Monograph
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  7. 7
    Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters

    Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters by Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah

    Published 2024
    “…The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. …”
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    Article
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  8. 8
    A New Mobile Malware Classification for SMS Exploitation

    A New Mobile Malware Classification for SMS Exploitation by Zaizi, NJM, Khailani, A, Madihah Mohd Saudi

    Published 2024
    journal::journal article
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  9. 9
    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak

    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

    Published 2019
    “…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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  10. 10
    A new mobile malware classification for SMS exploitation

    A new mobile malware classification for SMS exploitation by Zaizi N.J.M., Khailani A., Madihah Mohd Saudi

    Published 2024
    “…This research has developed a new mobile malware classification for Android smartphone using a covering algorithm. …”
    Conference Paper
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  11. 11
    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification

    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

    Published 2009
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    Monograph
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  12. 12
    Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi

    Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi by Rahimi, Ahmad Faris

    Published 2017
    “…The second one is to develop prototype for classification of credit cardholder behavior based on k Nearest Neighbors Algorithm. …”
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  13. 13
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…It is designed to incorporate with the CAPSOCA algorithm which intended to strengthen the classification outcomes. …”
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  14. 14
    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier

    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

    Published 2018
    “…There are two stages in the proposed classification system. Firstly, the 1D-LBP algorithm is used to extract the features of the normalized iris images and save the data in a text file according to the subject and the combinations to evaluate for the next stage. …”
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  15. 15
    Classification models for higher learning scholarship award decisions

    Classification models for higher learning scholarship award decisions by Wirawati Dewi Ahmad, Azuraliza Abu Bakar

    Published 2018
    “…Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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  16. 16
    Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi

    Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi by Che Alhadi, Nazirah

    Published 2012
    “…In order to develop this model classification and prototype, 480 Twitter’s messages were used as training data and 120 Twitter’s messages for testing data to determine the accuracy of the classification model. …”
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  17. 17
    A New Model For Network-Based Intrusion Prevention System Inspired By Apoptosis

    A New Model For Network-Based Intrusion Prevention System Inspired By Apoptosis by Mohammed Nadir bin Ali

    Published 2024
    thesis::doctoral thesis
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  18. 18
    Non-fiducial based electrocardiogram biometrics with kernel methods

    Non-fiducial based electrocardiogram biometrics with kernel methods by Hejazi, Maryamsadat

    Published 2017
    “…At classification level, Gaussian multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach is proposed to evaluate verification performance rates of the feature extraction algorithms. …”
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  19. 19
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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  20. 20
    Analysis and comparison of classification algorithms for credit approval in Islamic banks

    Analysis and comparison of classification algorithms for credit approval in Islamic banks by Pebrianti, Dwi, Wijanarko, Whena, Bayuaji, Luhur, Toha, Siti Fauziah

    Published 2025
    “…The results highlight significant performance differences among the algorithms. Random Forest and Decision Tree demonstrated strong training accuracy but suffered from overfitting, limiting their generalization to new data. …”
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