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Öğe A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy(Univ Suceava, Fac Electrical Eng, 2013) Hacibeyoglu, Mehmet; Arslan, Ahmet; Kahramanli, SirzatUsually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.Öğe The logic transformations for reducing the complexity of the discernibility function-based attribute reduction problem(Springer London Ltd, 2016) Hacibeyoglu, Mehmet; Salman, Mohammad Shukri; Selek, Murat; Kahramanli, SirzatThe basic solution for locating an optimal reduct is to generate all possible reducts and select the one that best meets the given criterion. Since this problem is NP-hard, most attribute reduction algorithms use heuristics to find a single reduct with the risk to overlook for the best ones. There is a discernibility function (DF)-based approach that generates all reducts but may fail due to memory overflows even for datasets with dimensionality much below the medium. In this study, we show that the main shortcoming of this approach is its excessively high space complexity. To overcome this, we first represent a DF of attributes by a bit-matrix (BM). Second, we partition the BM into no more than sub-BMs (SBMs). Third, we convert each SBM into a subset of reducts by preventing the generation of redundant products, and finally, we unite the subsets into a complete set of reducts. Among the SBMs of a BM, the most complex one is the first SBM with a space complexity not greater than the square root of that of the original BM. The proposed algorithm converts such a SBM with attributes into the subset of reducts with the worst case space complexity of .Öğe Predicting protein-protein interactions by weighted pseudo amino acid composition(Inderscience Enterprises Ltd, 2016) Goktepe, Yunus Emre; Ilhan, Ilhan; Kahramanli, SirzatProtein-protein interactions hold very important roles in biological processes. Prediction of PPIs is important for understanding these processes. In this context, substantive representations of proteins are needed during the process of interaction prediction in order to achieve higher prediction accuracy. In this paper, a new feature representation method, based on the concept of Chou's pseudo amino acid composition, was introduced. It is composed of the weighted amino acid composition information and the correlation factors of the protein. Finally, an SVM classification model was constructed for predicting PPIs. Experimental results exhibit that our method precedes those previously published in the literature.Öğe Predicting protein-protein interactions by weighted pseudo amino acid composition(Inderscience Enterprises Ltd, 2016) Goktepe, Yunus Emre; Ilhan, Ilhan; Kahramanli, SirzatProtein-protein interactions hold very important roles in biological processes. Prediction of PPIs is important for understanding these processes. In this context, substantive representations of proteins are needed during the process of interaction prediction in order to achieve higher prediction accuracy. In this paper, a new feature representation method, based on the concept of Chou's pseudo amino acid composition, was introduced. It is composed of the weighted amino acid composition information and the correlation factors of the protein. Finally, an SVM classification model was constructed for predicting PPIs. Experimental results exhibit that our method precedes those previously published in the literature.