2 research outputs found
Tracking changes using Kullback-Leibler divergence for the continual learning
Recently, continual learning has received a lot of attention. One of the
significant problems is the occurrence of \emph{concept drift}, which consists
of changing probabilistic characteristics of the incoming data. In the case of
the classification task, this phenomenon destabilizes the model's performance
and negatively affects the achieved prediction quality. Most current methods
apply statistical learning and similarity analysis over the raw data. However,
similarity analysis in streaming data remains a complex problem due to time
limitation, non-precise values, fast decision speed, scalability, etc. This
article introduces a novel method for monitoring changes in the probabilistic
distribution of multi-dimensional data streams. As a measure of the rapidity of
changes, we analyze the popular Kullback-Leibler divergence. During the
experimental study, we show how to use this metric to predict the concept drift
occurrence and understand its nature. The obtained results encourage further
work on the proposed methods and its application in the real tasks where the
prediction of the future appearance of concept drift plays a crucial role, such
as predictive maintenance.Comment: Accepted manuscript in SMC 2022, it will be published in the IEEE
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A nature-inspired biomarker for mental concentration using a single-channel EEG
We developed a system for measuring the attentional process during the performance of specific activities. The proposed biomarker device is able to estimate the mental concentration using a single-channel EEG. The system captures the EEG signal and several brain waves located in the left orbitofrontal brain region. Furthermore, we extended the input features of the system applying spectrum analysis. We applied two well-known evolutionary algorithms for selecting the best combination of input features: simulated annealing and geometric particle swarm optimization. Besides, we solved the binary classification problem (concentration vs. relaxation) using support vector machines and neural networks. Support vector machines are among the most common instruments for solving binary classification problems. On the other hand, we selected to study a family of neural networks named echo state networks, because the model is ideal for embedded systems and has shown good accuracy in real-world applications. The training and execution are fast, robust, and reliable. The developed system is autonomous, portable, reliable, non-invasive and has a low economic cost. Besides, it can be easily adjusted for each person and for each problem.Web of Scienc