Search Results - (( pattern classification problem algorithm ) OR ( using classification model algorithm ))

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  1. 1
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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  2. 2
    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
    “…On top of that, random forest ensemble classifier model has reported successive perform in most classification and pattern recognition problems. …”
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  3. 3
    Classification for large number of variables with two imbalanced groups

    Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

    Published 2020
    “…Both algorithms are beneficial towards the practitioners of classification predictive modelling as well as statisticians in pattern recognition domain.…”
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  4. 4
    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

    Published 2010
    “…In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. …”
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  5. 5
    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
    “…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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  6. 6
    Modern fuzzy min max neural networks for pattern classification

    Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

    Published 2019
    “…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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  7. 7
    Improved GART neural network model for pattern classification and rule extraction with application to power systems

    Improved GART neural network model for pattern classification and rule extraction with application to power systems by Yap K.S., Lim C.P., Au M.T.

    Published 2023
    “…Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. …”
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  8. 8
    Backpropagation algorithm for classification problem: academic performance prediction model for UiTM Melaka Mengubah Destini Anak Bangsa (MDAB) program. / Fadhlina Izzah Saman, Nurulhuda Zainuddin and Khairiyah Md Shahid

    Backpropagation algorithm for classification problem: academic performance prediction model for UiTM Melaka Mengubah Destini Anak Bangsa (MDAB) program. / Fadhlina Izzah Saman, Nur... by Saman, Fadhlina Izzah, Zainuddin, Nurulhuda, Md Shahid, Khairiyah

    Published 2012
    “…Artificial neural networks (ANN) has become one of the artificial intelligent techniques that has many successful examples when applied to classification problem such as doing pattern recognition and prediction. …”
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  9. 9
    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
    “…The aim of data mining is to search and find undetermined patterns in huge databases. A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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  10. 10
    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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  11. 11
    A new model for iris data set classification based on linear support vector machine parameter's optimization

    A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

    Published 2020
    “…Classification is a data analysis that extracts a model which describes an important data classes. …”
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  12. 12
    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The extraction network extracts detectors that represent pattern’s classes to be supplied to the classification network. …”
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  13. 13
    A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification

    A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification by Leow S.Y., Wong S.Y., Yap K.S., Yap H.J.

    Published 2023
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  14. 14
    Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

    Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification by Navan, Farhad Pourpanah

    Published 2015
    “…The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. …”
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  15. 15
    Neuro fuzzy classification and detection technique for bioinformatics problems

    Neuro fuzzy classification and detection technique for bioinformatics problems by Othman, Mohd. Fauzi, Moh, Thomas Shan Yau

    Published 2007
    “…A package of software based on neuro fuzzy model (ANFIS) has been developed using MATLAB software and optimization were done with the help from WEKA. …”
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  16. 16
    Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah

    Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

    Published 2021
    “…To solving pattern classification problem, the optimization deep learning architecture and parameter by using four convolution layers is set up to classify the three pathological signs; HEM, MA and exudate. …”
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  17. 17
    Household overspending model amongst B40, M40 and T20 using classification algorithm

    Household overspending model amongst B40, M40 and T20 using classification algorithm by Zulaiha Ali, Othman, Azuraliza, Abu Bakar, Nor Samsiah, Sani, Jamaludin, Sallim

    Published 2020
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  18. 18
    Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms

    Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

    Published 2024
    “…This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. …”
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  19. 19
    Improving hand written digit recognition using hybrid feature selection algorithm

    Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

    Published 2022
    “…While mRMR was capable of identifying a subset of features that were highly relevant to the targeted classification variable, it still carry the weakness of capturing redundant features along with the algorithm. …”
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  20. 20
    Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

    Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks by Muhammad Ashraq, Salahuddin

    Published 2013
    “…The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. …”
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