Physics

Pattern recognition (psychology)

Mathematics

Biology

Principle of maximum entropy

Time domain

Entropy (arrow of time)

Algorithm

Data mining

Filter (signal processing)

Machine learning

Time series

Mathematical analysis

Series (stratigraphy)

Paleontology

Maxima and minima

Artificial intelligence

Hilbert–Huang transform

Transfer entropy

Computer science

Feature extraction

Quantum mechanics

Data stream mining

Computer vision

Concept drift

Entropy

Most of time series deriving from complex systems in real life is non-stationary, where the data distribution would be influenced by various internal/external factors such that the contexts are persistently changing. Therefore, the concept drift detection of time series has practical significance. In this paper, a novel method called online entropy-based time domain feature extraction (ETFE) for concept drift detection is proposed. Firstly, the empirical mode decomposition based on extrema symmetric extension is used to decompose time series, where features in various time scales can be adaptively extracted. Meanwhile, the end point effect caused by traditional empirical mode decomposition can be avoided. Secondly, by using the entropy calculation, the time-domain features are coarse-grained to quantify the structure and complexity of the time series, among which six kinds of entropy are used for discussion. Finally, a statistical process control method based on generalized likelihood ratio is used to monitor the change of the entropy, which can effectively track the mean and amplitude of the time series. Therefore, the early alarm of concept drift can be given. Synthetic data sets and neonatal electroencephalogram (EEG) recordings with seizures annotations data sets are used to validate the effectiveness and accuracy of the proposed method.

The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection