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  1. 1
    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection

    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
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  2. 2
    Feature selection optimization using hybrid relief-f with self-adaptive differential evolution

    Feature selection optimization using hybrid relief-f with self-adaptive differential evolution by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Ahmad Nazri, Azree Shahrel, Mohamed, Raihani, Abd Manaf, Syaifulnizam

    Published 2017
    “…Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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  3. 3
    Class binarization with self-adaptive algorithm to improve human activity recognition

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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  4. 4
    Stock market turning points rule-based prediction / Lersak Photong … [et al.]

    Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

    Published 2021
    “…Feature selection was used to sort out key features for further classification. …”
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  5. 5
    Case Slicing Technique for Feature Selection

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

    Published 2004
    “…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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  6. 6
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…Feature selection and classification are widely utilized for data analysis. …”
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  7. 7
    Phylogenetic tree classification system using machine learning algorithm

    Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

    Published 2015
    “…This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. …”
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    Final Year Project Report / IMRAD
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  8. 8
    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 order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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  9. 9
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Chong, Hou Ming, Yin Yap, Xien, Seng Chia, Kim

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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  10. 10
    Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification

    Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

    Published 2022
    “…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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  11. 11
    Fusion of moment invariant method and deep learning algorithm for COVID-19 classification

    Fusion of moment invariant method and deep learning algorithm for COVID-19 classification by Ervin Gubin Moung, Chong, Joon Hou, Maisarah Mohd Sufian, Mohd Hanafi Ahmad Hijazi, Jamal Ahmad Dargham, Sigeru Omatu

    Published 2021
    “…This paper proposes a fusion of a moment invariant (MI) method and a DL algorithm for feature extraction to address the instabilities in the existing COVID-19 classification models. …”
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  12. 12
    A study on classification learning algorithms to predict crime status.

    A study on classification learning algorithms to predict crime status. by Shojaee, Somayeh, Mustapha, Aida, Sidi, Fatimah, A. Jabar, Marzanah

    Published 2013
    “…In this paper, we conducted an experiment to obtain better supervised classification learning algorithms to predict crime status by using two different feature selection methods tested on real dataset. …”
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  13. 13
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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  14. 14
    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam

    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

    Published 2017
    “…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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  15. 15
    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
    “…However, supervised learning technique has limitations for malware classification task. …”
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  16. 16
    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm by Iqbal, M.J., Faye, I., Said, A.M.D., Samir, B.B.

    Published 2017
    “…A statistical metric-based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. …”
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  17. 17
    A Machine Learning Classification Approach To Detect Tls-Based Malware Using Entropy-Based Flow Set Features

    A Machine Learning Classification Approach To Detect Tls-Based Malware Using Entropy-Based Flow Set Features by Keshkeh, Kinan

    Published 2022
    “…TLSMalDetect includes periodicity-independent entropy-based flow set (EFS) features produced by an FFT technique. The efficiency of EFS features is assessed in two ways: (1) by comparing them to the relevant related work’s features of outliers and flow using four feature importance methods, and (2) by analyzing the classification performance in the scenarios with and without EFS features. …”
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  18. 18
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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  19. 19
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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  20. 20
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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