Sirzat Kahramanli

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The 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(More)
Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with pertinent features would reduce the size of dataset and afterwards allow more apparent analysis of(More)
Obtaining the association between complex diseases and single nucleotide polymorphisms (SNPs) is one of the most important medical problems. Although obtaining the full set of SNPs is a very challenging issue, there are subsets of tag SNPs, each of which allows predicting the rest of SNPs with enough accuracy. Here, the problem is to obtain such a subset of(More)
Since the generation all of minimal subsets of attributes (MSAs) of a dataset is NP-hard, usually attribute reduction algorithms (ARAs) use some heuristics to find a small part of MSAs with the risk to overlook the best solutions. There is a discernibility function (DF)-based ARA for generating all MSAs, but often failing to find them even for medium sized(More)
In order to generate prime implicants for a given cube (minterm), most of minimization methods increase the dimension of this cube by removing one literal from it at a time. But there are two problems of exponential complexity. One of them is the selection of the order in which the literals are to be removed from the implicant at hand. The latter is the(More)