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

Abstract

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.

Maintaining and Monitoring AIOps Models Against Concept Drift

“…Out of the existing concept drift detectors for time series we can mention Feature Extraction Drift Detection (FEDD) [4] and Entropy-Based Time Domain Feature Extraction (ETFE) [9]. Both these detectors identify drift by extracting features from a given time series window and observing their similarity with the features extracted from the reference window.…”

Review of various feature extraction approaches for ERG signal analysis: Advantages and drawbacks

“…Conversely, amplitude represents the magnitude of the ERG response. Variations in amplitude can indicate changes in retinal sensitivity or the presence of abnormalities [21][22][23][24][25].…”

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

“…Classification of data streams requires a method for detecting and responding to a change in data distribution. This change (which is usually referred to as concept drift) could cause a modification to the current structure of the classification model [ 18 ]. Adaptive sliding window (ADWIN) is a popular tracking method with an adaptive size that changes in response to the change in the average of items [ 10 ].…”