Part 2. Special Issues in Intellectual Systems Theory

Autoassociative neural networks in a classification problem with truncated dataset

A. A. Khusaenov

Moscow State University, faculty of Mechanics and Mathematics

Abstract: The adverse clinical outcome risk assessing model is considering. It is proposed the unsupervised learning method application for a binary classification problem with a single answer training set. A truncated set is a dataset with a small examples number of one of the classes (favorable or unfavorable). A truncated set is also the data obtained after original table clearing. Some results of this model application are presented in a research [1] conducted by the scientists of the National Research Center for Therapy and Preventive Medicine of the Ministry of Health of the Russian Federation and the scientists of the Faculty of Mechanics and Mathematics of the Lomonosov Moscow State University. It is proposed the general method for such problems.

Keywords: neural networks, unsupervised learning, Autoassociative neural networks, autoencoder, adverse clinical outcome.