Abstract
Methods of machine learning and data mining are becoming the cornerstone in information technologies with real-time image and video recognition methods getting more and more attention. While computational system architectures are getting larger and more complex, their learning methods call for changes, as training datasets often reach tens and hundreds of thousands of samples, therefore increasing the learning time of such systems. It is possible to reduce computational costs by tuning the system structure to allow fast, high accuracy learning algorithms to be applied. This paper proposes a system based on extended multidimensional neo-fuzzy units and its learning algorithm designed for data streams processing tasks. The proposed learning algorithm, based on the information entropy criterion, has significantly improved the system approximating capabilities. Experiments have confirmed the efficiency of the proposed system in solving real-time video stream recognition tasks.
Highlights
Classification and clustering relate to the main tasks in data stream analysis
Deep Neural Networks (DNN) [8,9,10,11] have been given in terms of classification accuracy. This line of research turned out to be very promising, convolutional neural networks (CNN) and deep learning are widely used in the pattern recognition tasks, especially for image, audio and video streams classifying and clustering
It is important to note that a neo-fuzzy system learning rate can be optimized [37], which allows using it in real-time Data Stream Mining tasks
Summary
Classification and clustering relate to the main tasks in data stream analysis. They mean the distribution of objects between groups with not known properties in advance. Deep Neural Networks (DNN) [8,9,10,11] have been given in terms of classification accuracy This line of research turned out to be very promising, convolutional neural networks (CNN) and deep learning are widely used in the pattern recognition tasks, especially for image, audio and video streams classifying and clustering. In [17], the mixed fuzzy clustering algorithm for health care problems where time series analysis is necessary was proposed Another hybrid structure discussed in [18] is the deep TSK classifier which uses interpreted linguistic rules for a fuzzy inference system. The high training speed of the hybrid systems requires the use of non-standard neurons, architectures and teaching methods.
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