Yazar "Ibrahim, Mohammed Hussein" seçeneğine göre listele

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    Improved Nelder-Mead Optimization Method in Learning Phase of Artificial Neural Networks
    (2018) Koçer, Hasan Erdinç; Merdan, Mustafa; Ibrahim, Mohammed Hussein
    Artificial neural networks method is the most important/preferred classification algorithm in machine learning area. The weightson the nets in artificial neural directly affect the classification accuracy of artificial neural networks. Therefore, finding optimum values ofthese weights is a difficult optimization problem. In this study, the Nelder-Mead optimization method has been improved and used fortraining of artificial neural networks. The optimum weights of artificial neural networks are determined in the training stage. Theperformance of the proposed improved Nelder-Mead-Artificial neural networks classification algorithm has been tested on the mostcommon datasets from the UCI machine learning repository. The classification results obtained from the proposed improved Nelder-Mead-Artificial neural networks classification algorithm are compared with the results of the standard Nelder-Mead-Artificial neural networksclassification algorithm. As a result of this comparison, the proposed improved Nelder-Mead-Artificial neural networks classificationalgorithm has given best results in all datasets.
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    A novel switching function approach for data mining classification problems
    (Springer, 2020) Ibrahim, Mohammed Hussein; Hacibeyoglu, Mehmet
    Rule induction (RI) is one of the known classification approaches in data mining. RI extracts hidden patterns from instances in terms of rules. This paper proposes a logic-based rule induction (LBRI) classifier based on a switching function approach. LBRI generates binary rules by using a novel minimization function, which depends on simple and powerful bitwise operations. Initially, LBRI generates instance codes by encoding the dataset with standard binary code and then generates prime cubes (PC) for all classes from the instance codes by the proposed reduced offset method. Finally, LBRI selects the most effective PC of the current classes and adds them into the binary rule set that belongs to the current class. Each binary rule represents an If-Then rule for the rule induction classifiers. The proposed LBRI classifier is based on basic logic functions. It is a simple and effective method, and it can be used by intelligent systems to solve real-life classification/ prediction problems in areas such as health care, online/financial banking, image/voice recognition, and bioinformatics. The performance of the proposed algorithm is compared to six rule induction algorithms; decision table, Ripper, C4.5, REPTree, OneR, and ICRM by using nineteen different datasets. The experimental results show that the proposed algorithm yields better classification accuracy than the other rule induction algorithms on ten out of nineteen datasets.