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Subject: search.filters.subject.Support vector machine

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    Comparative study of binary classifiers for reducing false negative detection of melanoma in skin lesions
    (IEEE, 2022-10-27) Jooravan, Amith; Reddy, Serendra; Pillay, Nelendran
    Reliable and accurate classification of a skin lesion is essential to the early diagnosis of skin cancer, especially melanoma. Traditional classification methods require performing a biopsy on the lesion. The overlap of benign and malignant clinical features may lead to incorrect melanoma diagnosis and/or excising an excessive number of benign lesions. This paper focuses on the use of machine learning to aid physicians with the non-invasive classification methodology of skin lesions, whilst prioritising the minimization of false negative classification. The clinical features used are based on the ABCD rule, representing the asymmetry, border, colour and diameter of the lesion. The dermoscopic images chosen are of melanoma lesions less than 0,76mm in thickness which corresponds to the early stages of cancer. The investigated classification methods include K-Nearest neighbours (KNN), Naïve Bayes and linear support vector machine. (LSVM). This research proposes the use of a LSVM machine learning algorithm to classify a skin lesion as being either melanoma or non-melanoma with the lowest false negative rate of the investigated classification. Classification accuracy of 85% and a false negative rate of 5% is achieved.
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    High impedance fault detection protection scheme for power systems distribution networks
    (Elsevier BV, 2022) Moloi, Katleho; Davidson, Innocent
    Protection schemes are used in safe‐guarding and ensuring the reliability of an electrical power network. Developing an effective protection scheme for high impedance fault (HIF) detection remains a challenge in research for protection engineers. The development of an HIF detection scheme has been a subject of interest for many decades and several methods have been proposed to find an optimal solution. The conventional current‐based methods have technical limitations to ef‐ fectively detect and minimize the impact of HIF. This paper presents a protection scheme based on signal processing and machine learning techniques to detect HIF. The scheme employs the discrete wavelet transform (DWT) for signal decomposition and feature extraction and uses the support vec‐ tor machine (SVM) classifier to effectively detect the HIF. In addition, the decision tree (DT) classi‐ fier is implemented to validate the proposed scheme. A practical experiment was conducted to ver‐ ify the efficiency of the method. The classification results obtained from the scheme indicated an accuracy level of 97.6% and 87% for the simulation and experimental setups. Furthermore, we tested the neural network (NN) and decision tree (DT) classifiers to further validate the proposed method
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    ANN’s vs. SVM’s for image classification
    (International ASET, 2012-08) Moorgas, Kevin Emanuel; Pillay, Nelendran; Governder, Poobalan
    In this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.