الفهرس يوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
This thesis introduces a new algorithm termed as Rough Fuzzy C-Medoids based on Randomized Search (RFCMRANS) algorithm, to select the most informative bio-bases; the minimum set of bio-bases with maximum information. As amino acids are nonnumeric variables, they need encoding before using them as inputs for any pattern recognition algorithm. Therefore, a bio-basis function has been used to map a nonnumeric sequence space to a numerical feature space. It is designed using an amino acid mutation matrix. The (RFCMRANS) algorithm is comprised of a judicious integration of the principles of rough sets, fuzzy sets, the C-Medoids based on randomized search algorithm, and the amino acid mutation matrix. While the membership function of fuzzy sets enables efficient handling of overlapping partitions, the concept of lower and upper bounds of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. The concept of crisp lower bound and fuzzy boundary of class, introduced in RFCMRANS, enables efficient selection of the minimum set of the most informative bio-bases. Some quantitative indices are used for evaluating quantitatively the quality of selected bio-bases. The effectiveness of the proposed algorithm, along with a comparison with other algorithms, has been demonstrated on dfferent types ofprotein data sets.