Yazar "Erkan, Uğur" seçeneğine göre listele

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    A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices
    (Springer Science and Business Media Deutschland GmbH, 2022) Memiş, Samet; Enginoğlu, Serdar; Erkan, Uğur
    Fuzzy parameterized fuzzy soft matrices (fpfs-matrices) which can model problems involving fuzzy objects and parameters are one of the mathematical tools used to deal with decision-making problems. To utilize soft decision-making methods via fpfs-matrices in machine learning is likely to draw much scholarly attention. In this paper, we propose Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) in order to transfer modeling success of fpfs-matrices to machine learning. We then compare FPFS-CMC with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in consideration of accuracy, precision, recall, macro-F-score, and micro-F-score performance metrics, and 15 datasets in UCI Machine Learning Repository. Besides, we compare the proposed classifier with the state-of-the-art Support Vector Machine (SVM), Decision Tree (DT), and Adaptive Boosting (AdaBoost) in terms of five performance metrics herein. Afterward, the results from the experiments are analyzed by employing the Friedman and Nemenyi tests to assess the statistical significance of the differences in performances. Both experimental and statistical results show that FPFS-CMC outperforms the others. Finally, we provide the conclusive remarks and some suggestions for further research.
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    A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices
    (2022) Memış, Samet; Enginoğlu, Serdar; Erkan, Uğur
    Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass),\rhas been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest\rneighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy\rparameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM)\rand classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity\rmeasures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure.\rThen, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies\rthe given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success\rof our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN,\rEigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time.\rMoreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that\rFPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.
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    Adaptive cesáro mean filter for salt-and-pepper noise removal
    (TUBITAK, 2020) Enginoğlu, Serdar; Erkan, Uğur; Memiş, Samet
    In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research. © 2020, TUBITAK. All rights reserved.
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    Adaptive Right Median Filter for Salt-and-Pepper Noise Removal
    (2019) Erkan, Uğur; Gökrem, Levent; Enginoğlu, Serdar
    In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median (rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.
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    Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images
    (2021) Memış, Samet; Erkan, Uğur
    This paper proposes a new filter, Different Adaptive Modified Riesz Mean Filter (DAMRmF), for high-density salt-and-pepper noise (SPN) removal. DAMRmF operationalizes a pixel weight function and adaptivity condition of Adaptive Median Filter (AMF). In the simulation, the proposed filter is compared with Adaptive Frequency Median Filter (AFMF), Three-Values-Weighted Method (TVWM), Unbiased Weighted Mean Filter (UWMF), Different Applied Median Filter (DAMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesáro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF) for 20 traditional test images with noise levels from 60% to 90%. The results show that DAMRmF outperforms the state-of-the-art filters in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values. Moreover, DAMRmF also performs better than the state-of-the-art filters concerning mean PSNR and SSIM results. We finally discuss DAMRmF for further research.
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    Exponentially Weighted Mean Filter for Salt-and-Pepper Noise Removal
    (Springer Science and Business Media Deutschland GmbH, 2022) Enginoğlu, Serdar; Erkan, Uğur; Memiş, Samet
    This paper defines an exponentially weighted mean using an exponentially decreasing sequence of simple fractions based on distance. It then proposes a cutting-edge salt-and-pepper noise (SPN) removal filter—i.e., Exponentially Weighted Mean Filter (EWmF). The proposed method incorporates a pre-processing step that detects noisy pixels and calculates threshold values based on the possible noise density. Moreover, to denoise the images operationalizing the calculated threshold values, EWmF employs the exponentially weighted mean (ewmean) in 1-approximate Von Neumann neighbourhoods for low noise densities and k-approximate Moore neighbourhoods for middle or high noise densities. Furthermore, it ultimately removes the residual SPN in the processed images by relying on their SPN densities. The numerical and visual results obtained with MATLAB R2021a manifest that EWmF outperforms nine state-of-the-art SPN filters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices
    (Institute of Electrical and Electronics Engineers, 2021) Memiş, Samet; Enginoğu, Serdar; Erkan, Uğur
    In this paper, we first define eight pseudo-metrics and eight pseudo-similarities based on these pseudo-metrics overfpfs-matrices. We then propose a new classification algorithm, i.e. Fuzzy Parameterized Fuzzy Soft Euclidean Classifier (FPFS-EC), based on Euclidean pseudo-similarity. After that, we compare FPFS-EC with Support Vector Machines (SVM), Fuzzy k-Nearest Neighbor (Fuzzy kNN), Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy kNN Based on the Bonferroni Mean (BM-Fuzzy kNN) in terms of the performance criteria - namely accuracy, precision, recall, macro F-score, and micro F-score - and running time by using 18 real-world datasets in the UCI machine learning repository. The results show that FPFS-EC performs better in the occurrence of the 13 of 18 datasets in question than SVM, Fuzzy kNN, FSSC, FussCyier, HDFSSC, and BM-Fuzzy kNN.