Integration of Results from Recognition Algorithms Applied to the Uranium Deposits
Ravil I. Muhamediyev*1,*2, Yedilkhan Amirgaliyev*2,
Syrymbet Kh. Iskakov*1, Yan I. Kuchin*3,
and Elena Muhamediyeva*4
*1International IT University, 34A/8A, Manas str./Zhandosov str., Almaty 050040, Kazakhstan
*2Institute of Problems of Information and Control, Ministry of Education and Science of the Republic of Kazakhstan, 125 Pushkina str., Almaty 050010, Kazakhstan
*3LLC Geotechnoservice, Kazatomprom, 156-156a, Bogenbay-batyr str., Almaty 050020, Kazakhstan
*4Riga Technical University, 1 Kaļķu str., Riga LV-1658, Latvia
Data interpretation of electric logging can be performed using self-learning systems such as artificial neural networks (ANNs). Preliminary research shows that by using ANN we can achieve 52-73% of coincidence of interpretable data and experimental results. Therefore, it is necessary to analyze the possibility of using other classification algorithms, and that of using several classification algorithms simultaneously through a unified system (referred to as an integrator. These algorithms may improve the quality of recognition of individual species. The problem of developing a recognition system that combines several classification algorithms, also known as the integrator, is formulated here. A simple algorithm is developed for the learning and recognition of an integrator for the post processing stage; this enhances the recognition accuracy by 1-3%.
Syrymbet Kh. Iskakov, Yan I. Kuchin, and
and Elena Muhamediyeva, “Integration of Results from Recognition Algorithms Applied to the Uranium Deposits,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.3, pp. 347-352, 2014.
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