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  1. 1
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…This algorithm was compared to co-spectral plot method for labelling clusters the clusters generated in Landsat TM dataset. …”
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  2. 2
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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  3. 3
    Multi-label learning based on positive label correlations using predictive apriori

    Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

    Published 2019
    “…Multi-label Learning (MLL) is a general task in data mining that consists of three main tasks: classification, label ranking, and multi-label ranking. …”
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  4. 4
    Multi label ranking based on positive pairwise correlations among labels

    Multi label ranking based on positive pairwise correlations among labels by Alazaidah, Raed, Ahmad, Farzana Kabir, Mohsin, Mohamad

    Published 2020
    “…Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. …”
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  5. 5
    Application of Optimization Methods for Solving Clustering and Classification Problems

    Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

    Published 2011
    “…The focus of this thesis is on solvingclustering and classification problems. Specifically, we will focus on new optimization methods for solving clustering and classification problems. …”
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  6. 6
    Enhancing Classification Algorithms with Metaheuristic Technique

    Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

    Published 2024
    “…For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. …”
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  7. 7
    The classification of FTIR plastic bag spectra via label spreading and stacking

    The classification of FTIR plastic bag spectra via label spreading and stacking by Almanifi, Omair Rashed Abdulwareth, Ng, Jee Kwan, Anwar P. P., Abdul Majeed

    Published 2021
    “…Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. …”
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  8. 8
    Knowledge base processing method based on text classification algorithm

    Knowledge base processing method based on text classification algorithm by Baisheng Zhong, Mohd Shamrie Sainin, Tan Soo Fun

    Published 2023
    “…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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  9. 9
    Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm

    Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm by Dhewa, Oktaf Agni, Utama, Safitri Yuliana, Nasuha, Aris, Gunawan, Teddy Surya, Pratama, Gilang Nugraha Putu

    Published 2024
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  10. 10
    Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout

    Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

    Published 2025
    “…The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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  11. 11
    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks

    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks by Ihsanto, Eko, Ramli, Kalamullah, Sudiana, Dodi, Gunawan, Teddy Surya

    Published 2020
    “…Many algorithms have been developed for automated electrocardiogram (ECG) classification. …”
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  12. 12
    Improved method of classification algorithms for crime prediction

    Improved method of classification algorithms for crime prediction by Babakura, Abba, Sulaiman, Md. Nasir, Yusuf, Mahmud Ahmad

    Published 2014
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  13. 13
    AN INTEGRATED GENERIC TEXT CLASSIFICATION ALGORITHM FOR INDONESIAN AND MALAY NEWS DOCUMENTS

    AN INTEGRATED GENERIC TEXT CLASSIFICATION ALGORITHM FOR INDONESIAN AND MALAY NEWS DOCUMENTS by ,, ZUL INDRA

    Published 2016
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  14. 14
    Classification of JPEG files by using extreme learning machine

    Classification of JPEG files by using extreme learning machine by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…The algorithm automatically classifies the files based on evaluation measures of three methods Entropy, Byte Frequency Distribution and Rate of Change. …”
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  15. 15
    Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd

    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. …”
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  16. 16
    Extreme learning machine classification of file clusters for evaluating content-based feature vectors

    Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy.…”
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  17. 17
    A category classification algorithm for Indonesian and Malay news documents

    A category classification algorithm for Indonesian and Malay news documents by Jaafar, J., Indra, Z., Zamin, N.

    Published 2016
    “…Experiments were conducted using 280 Indonesian and 280 Malay online news documents from the year 2014-2015. The classification method is proven to produce a good result with accuracy rate of up to 95.63 for language identification, and 97.5 for category classification. …”
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  18. 18
    Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling

    Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling by Mohamed, Raihani

    Published 2018
    “…Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. …”
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  19. 19
    A direct ensemble classifier for learning imbalanced multiclass data

    A direct ensemble classifier for learning imbalanced multiclass data by Samry @ Mohd Shamrie Sainin

    Published 2013
    “…Thus, an ensemble of classifiers is one of the methods used to solve multiclass classification tasks. …”
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  20. 20
    Multi-label incremental kernel extreme earning machine for food recognition / Chen Sai

    Multi-label incremental kernel extreme earning machine for food recognition / Chen Sai by Chen , Sai

    Published 2022
    “…We applied Inception-Resnet-V2 for food feature extraction and the Relief F method for feature ranking and selection. Then used ARCIKELM-ML for multi-label classification. …”
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