Browsing by Subject "classification algorithm"

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    A stochastic gradient descent based SVM with fuzzy-rough feature selection and instance selection for breast cancer diagnosis
    (American Scientific Publishers, 2015) Onan A.
    Breast cancer remains to be one of the most severe and deadly diseases among women in the world. Fortunately, a long survival rate for patients with not metastasized breast cancer can be achieved with the help of early detection, proper treatment and therapy. This urges the need to develop efficient classification models with high predictive performance. Machine learning and artificial intelligence based methods are effectively utilized for building classification models in medical domain. In this paper, fuzzy-rough feature selection based support vector machine classifier with stochastic gradient descent learning is proposed for breast cancer diagnosis. In the proposed model, fuzzy-rough feature selection with particle swarm optimization based search is used for obtaining a subset of relevant features for model. In order to select appropriate instances, a fuzzy-rough instance selection method is utilized. The effectiveness of the proposed classification approach is evaluated on Wisconsin Breast Cancer Dataset (WBCD) with classification evaluation metrics, such as classification accuracy, sensitivity, specificity, F-measure and kappa statistics. Experimental results indicate that the proposed model can achieve a very high predictive performance. Copyright © 2015 American Scientific Publishers.
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    Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling
    (Hindawi Limited, 2018) Onan A.
    Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods. © 2018 Aytuǧ Onan.
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    Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures
    (Elsevier Ltd, 2023) Acar Demirci B.; Demirci O.; Engin M.
    Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features. © 2023 Elsevier Ltd