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  1. 1
    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

    Published 2023
    “…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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  2. 2
    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)

    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224) by Mahat, Nor Idayu, Engku Abu Bakar, Engku Muhammad Nazri, Zakaria, Ammar, Mohd Nazir, Mohd Amril Nurman, Misiran, Masnita

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  3. 3
    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification

    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

    Published 2012
    “…The proposed system utilizes Biased ARTMAP for pattern learning and classification. The ARTMAP system is dependent on training sequence presentation to determine the effectiveness of the learning processes, as well as the strength of the biasing parameter, lambda λ. …”
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  4. 4
    Information Theoretic-based Feature Selection for Machine Learning

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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  5. 5
    An evolutionary based features construction methods for data summarization approach

    An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

    Published 2015
    “…Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. …”
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    Research Report
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  6. 6
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…Widespread models like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy (ES) and Population-Based Incremental Learning (PBIL) dealing with the specified problems are also explored and compared. …”
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  7. 7
    Case Slicing Technique for Feature Selection

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

    Published 2004
    “…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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  8. 8
    Phylogenetic tree classification system using machine learning algorithm

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

    Published 2015
    “…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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    Final Year Project Report / IMRAD
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  9. 9
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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  10. 10
    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
    “…The problems in applying unsupervised learning/clustering is that this method requires teacher during the classification process and it has to learn independently which may lead to poor classification. …”
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  11. 11
    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms

    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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    Final Year Project
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  12. 12
    Autism Spectrum Disorder Classification Using Deep Learning

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. …”
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  13. 13
    Waste management using machine learning and deep learning algorithms

    Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

    Published 2020
    “…So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. …”
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  14. 14
    Functional link neural network with modified bee-firefly learning algorithm for classification task

    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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  15. 15
    An improved algorithm for iris classification by using support vector machine and binary random machine learning

    An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

    Published 2018
    “…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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  16. 16
    Using KNN algorithm for classification of textual documents

    Using KNN algorithm for classification of textual documents by Moldagulova A., Sulaiman R.B.

    Published 2023
    Conference Paper
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  17. 17
    An automated strabismus classification using machine learning algorithm for binocular vision management system

    An automated strabismus classification using machine learning algorithm for binocular vision management system by Rohismadi, Muhammad Amirul Isyraf, Mat Raffei, Anis Farihan, Zulkifli, Nor Saradatul Akmar, Ithnin, Mohd. Hafidz, Othman, Shah Farez

    Published 2023
    “…To overcome these limitations, a machine learning algorithm, which is a case-based reasoning, is developed to automate the strabismus classification. …”
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  18. 18
    Performance comparison of CNN and LSTM algorithms for arrhythmia classification

    Performance comparison of CNN and LSTM algorithms for arrhythmia classification by Hassan, S.U., Zahid, M.S.M., Husain, K.

    Published 2020
    “…Among the existing deep learning model, convolutional neural network (CNN) and long short-term memory (LSTM) algorithms are extensively used for arrhythmia classification. …”
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  19. 19
    A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification

    A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification by Madanan M., Gunasekaran S.S., Mahmoud M.A.

    Published 2024
    “…For machine learning, SVM is a very good classification model. …”
    Conference Paper
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  20. 20
    Evaluation of the Transfer Learning Models in Wafer Defects Classification

    Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

    Published 2022
    “…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
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