Algorithms for Automated Differentiation of Subtypes and Improving the Overall Accuracy of Image Classification in Career Guidance

Abstract

The paper discusses the problems and prospects of implementing a hybrid classifier for career guidance testing according to the author's method «Associative color space». The authors set themselves the following goals: to increase the accuracy of determining the main classes and to identify subtypes that have visual similarities, but significant differences in terms of the characteristics of the work style. To improve the accuracy of determining the problem class of images, 11 special filters were developed and implemented in order to identify implicit features of the class. The use of these filters additionally solved the problem of distinguishing visually similar subtypes of the two main classes, which makes it possible to further automate not only the definition of the main class with optimal accuracy, but also the subtype, which will allow for a more thorough diagnosis of career guidance and provide the subjects with more specific characteristics and recommendations regarding their work style. Analyzing the results of the experiments, the authors hypothesized about the possibility of creating a three-dimensional map of types and subtypes, where there could be areas of pronounced types, subtypes, as well as transitional types with special characteristics that allow avoiding the result of a «mixed» type for the subjects.