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COAR Access Rights: metadata only accessSubject: Decision support systems

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    Article
    Citation - WoS: 19
    Citation - Scopus: 24
    Increasing Accuracy of Two-Class Pattern Recognition With Enhanced Fuzzy Functions
    (Pergamon-elsevier Science Ltd, 2009) Celikyilmaz, Asli; Tuerksen, I. Burhan; Aktas, Ramazan; Doganay, M. Mete; Ceylan, N. Basak
    In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems. (c) 2007 Elsevier Ltd. All rights reserved.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 1
    A Mathematical Model Proposal for Cost-Effective Course Planning in Large Hierarchical Organizations
    (Elsevier, 2014) Karamalak, Levent; Sabuncuoglu, Ihsan; Ozkil, Altan
    Hierarchical organizations, especially in government agencies, are known by their pyramidal structures and continuous training needs resulting from promotions and/or assignments. Using scientific and rational methods in the job analysis/description, recruitment/selection, assignment, performance appraisal and career planning functions of human resource management (HRM) process decreases training costs. In this study, we develop a new chain of methodologies (the cost-effective course planning model (CECPM)) to decrease training costs and increase the level of specialization. This methodology is implemented in the following steps of the HRM process: (1) the job analysis/description step, where our Mission Description Matrix defines in measurable units the amount of training needed for an employee assigned to a position, (2) the career matrix step, where the minimum training costs for an employee's career path are determined using our network-flow model and (3) the assignment step, where we propose a decision support system composed of an analytical hierarchy process, linear programming and Pareto optimality analysis. The results indicate that our proposed system ensures minimum training needs while satisfying person-to-position compatibility and personnel's preferences. (C) 2014 Elsevier B.V. All rights reserved.