ІНТЕЛЕКТУАЛЬНА ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ШВИДКОЇ КЛАСИФІКАЦІЇ ЗА УМОВ ПЕРЕТИННИХ КЛАСІВ

The subject of this research is the process of rapid data classification under conditions of overlapping classes. Rapid classification is performed in real-time or near-real-time mode. The aim of the work is to develop an intelligent information technology for rapid classification in online and nearline modes under conditions of overlapping classes. Achieving this goal allows for the consideration of non-stationarity in input data and class imbalance under conditions of streaming data. The tasks of compensating for noise in input data and changes in input data distribution due to non-stationarity, as well as the task of compensating for class imbalance, are interconnected when classifying under conditions of overlapping classes and require the development of a comprehensive solution. To achieve the goal, the following tasks are addressed: structuring approaches to classification of overlapping classes considering non-stationarity in input data and class imbalance; developing an intelligent technology for classification in online and nearline modes. An intelligent information technology for rapid classification under conditions of overlapping classes is proposed. The technology includes stages of preliminary classification considering noise in input data, classification considering class imbalance, and classification considering changes in input data patterns. The technology involves sequential use of a neo-fuzzy system, an adaptive neuro-fuzzy system, and a multilayer neural network with kernel bell-shaped activation functions. The neo-fuzzy system uses neo-fuzzy neurons, ensuring resistance to noise. The adaptive neuro-fuzzy system considers distances between input data and class centers in feature space, ensuring classification under class imbalance conditions. The multilayer neural network with kernel bell-shaped activation functions uses a recurrent learning algorithm, ensuring adaptation to new data with a new distribution. The technology enables rapid iterative refinement of classification decisions according to changes in input data characteristics.