Abstract
The paper dwells on the analysis of the efficiency of different algorithms for numerical expert data processing. Five methods were chosen for comparison: (1) on the basis of mean values; (2) on the basis of mean values with regard to experts’ competence assessment; (3) by means of getting average values on the basis of the maximum likelihood estimation method; (4) by means of getting median values on the basis of the maximum likelihood estimation method; (5) by means of getting median values on the basis of the least squares method. A complex criterion, based on evaluation of the degree of experts’ opinion consistency, the degree of closeness of the obtained results to their true values and the degree of convenience of the obtained results for solving specific tasks, is chosen as an efficiency criterion. The paper presents procedures of estimation of the mentioned components of the complex criterion and derivation of a complex estimate on the basis of known estimates of individual components. The experiment showed that, in the context of assessment of importance of individual PC components under the process of information security provision, the best result of the expert data processing is obtained with the use of the maximum likelihood estimation method on the basis of mean values, and the worst result is obtained with the use of the classical processing method on the basis of mean values. It is proposed to apply a procedure of getting a resulting estimate on the basis of a balanced consideration of the estimates obtained through different methods.
The reported study was funded by RFBR according to the research project â„– 18-37-00130.
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Popov, G.A., Kvyatkovskaya, I.Y., Zholobova, O.I., Kvyatkovskaya, A.E., Chertina, E.V. (2019). Making a Choice of Resulting Estimates of Characteristics with Multiple Options of Their Evaluation. In: Kravets, A., Groumpos, P., Shcherbakov, M., Kultsova, M. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2019. Communications in Computer and Information Science, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-030-29743-5_7
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