Yazar "Ibrahim, Mohammed H." seçeneğine göre listele

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    EF_Unique: An Improved Version of Unsupervised Equal Frequency Discretization Method
    (Springer Heidelberg, 2018) Hacibeyoglu, Mehmet; Ibrahim, Mohammed H.
    Discretization is an important data preprocessing technique used in data mining and knowledge discovery processes. The purpose of discretization is to transform or partition continuous values into discrete ones. In this manner, many data mining classification algorithms can be applied the discrete data more concisely and meaningfully than continuous ones, resulting in better performance. In this study, an improved version of the unsupervised equal frequency (EF) discretization method, EF_Unique, is proposed for enhancing the performance of discretization. The proposed EF_Unique discretization method is based on the unique values of the attribute to be discretized. In order to test the success of the proposed method, 17 benchmark datasets from the UCI repository and four data mining classification algorithms were used, namely Naive Bayes, C.45, k-nearest neighbor, and support vector machine. The experimental results of the proposed EF_Unique discretization method were compared with those obtained using well-known discretization methods; unsupervised equal width (EW), EF, and supervised entropy-based ID3 (EB-ID3). The results show that the proposed EF_Unique discretization method outperformed EW, EF, and EB-ID3 discretization methods in 43, 41, and 27 out of the 68 benchmark tests, respectively.
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    Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm
    (Springer Heidelberg, 2022) Ibrahim, Mohammed H.; Hacibeyoglu, Mehmet; Agaoglu, Afsin; Ucar, Fikret
    Glaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindness. Nowadays, deep learning classification algorithms are widely used to diagnose various diseases. However, in general, the training of deep learning algorithms is carried out by traditional gradient-based learning techniques that converge slowly and are highly likely to fall to the local minimum. In this study, we proposed a novel decision support system based on deep learning to diagnose glaucoma. The proposed system has two stages. In the first stage, the preprocessing of glaucoma disease data is performed by normalization and mean absolute deviation method, and in the second stage, the training of the deep learning is made by the artificial algae optimization algorithm. The proposed system is compared to traditional gradient-based deep learning and deep learning trained with other optimization algorithms like genetic algorithm, particle swarm optimization, bat algorithm, salp swarm algorithm, and equilibrium optimizer. Furthermore, the proposed system is compared to the state-of-the-art algorithms proposed for the glaucoma detection. The proposed system has outperformed other algorithms in terms of classification accuracy, recall, precision, false positive rate, and F1-measure by 0.9815, 0.9795, 0.9835, 0.0165, and 0.9815, respectively.
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    A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training
    (Hindawi Ltd, 2018) Hacibeyoglu, Mehmet; Ibrahim, Mohammed H.
    Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novelmultimean particle swarmoptimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.
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    ODBOT: Outlier detection-based oversampling technique for imbalanced datasets learning
    (Springer London Ltd, 2021) Ibrahim, Mohammed H.
    In many real-world problems, the datasets are imbalanced when the samples of majority classes are much greater than the samples of minority classes. In general, machine learning and data mining classification algorithms perform poorly on imbalanced datasets. In recent years, various oversampling techniques have been developed in the literature to solve the class imbalance problem. Unfortunately, few of the oversampling techniques can be spread to tackle the relationship between the classes and use the correlation between attributes. Moreover, in most cases, the existing oversampling techniques do not handle multi-class imbalanced datasets. To this end, in this paper, a simple but effective outlier detection-based oversampling technique (ODBOT) is proposed to handle the multi-class imbalance problem. In the proposed ODBOT, the outlier samples are detected by clustering within the minority class(es), and then, the synthetic samples are generated by consideration of these outlier samples. The proposed ODBOT generates very efficient and consistent synthetic samples for the minority class(es) by analyzing well the dissimilarity relationships among attribute values of all classes. Moreover, ODBOT can reduce the risk of the overlapping problem among different class regions and can build a better classification model. The performance of the proposed ODBOT is evaluated with extensive experiments using commonly used 60 imbalanced datasets and five classification algorithms. The experimental results show that the proposed ODBOT oversampling technique consistently outperformed the other common and state-of-the-art techniques in terms of various evaluation criteria.
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    WBBA-KM: A hybrid weight-based bat algorithm with the k-means algorithm for cluster analysis
    (Gazi Univ, 2022) Ibrahim, Mohammed H.
    Data clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.