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%.
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