Time series adapted supervised fuzzy discretization: an application to ECG signals

Orhan, Umut

doi:10.3906/elk-1411-36

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…This method is able to improve the analytical efficiency without scarifying accuracy. In this paper, we incorporate the modified hierarchical clustering (HC)-based algorithm with the PLA, which generates fuzzy membership values [20] and allows overlapping of boundaries of intervals. Algorithm 2 shows the modified HC process, which smooths the data items within small interval times.…”

Section: Data Optimization Process In Pdamentioning

confidence: 99%

EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control

Zhang

Lee

et al. 2020

Electronics

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Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain–computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals associated with specific activities. This paper proposes an optimized data sampling model to identify the status of the human brain and further discover brain activity patterns. The sampling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data sampling methods; and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data sampling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related sampling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control. The experimental results indicate that the NDA–PDA segments with less than 10% of the original data size can achieve 98% accuracy, as compared with original data sets. The FP method identifies more than 12 common patterns for EEG data analysis based on the optimized sampling methods.

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Section: Data Optimization Process In Pdamentioning

confidence: 99%

EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control

Zhang

Lee

et al. 2020

Electronics

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“…Taijun et al [21] propose a post-processing method for improving the quality of discretization adjusting the boundary points of interval in order to obtain a positive influence on the attribute. Supervised fuzzy discretization for classifying time series datasets is proposed in [22]. This method can be used without having expertise on data.…”

Section: Related Workmentioning

confidence: 99%

Improvement of the Accuracy of Prediction Using Unsupervised Discretization Method: Educational Data Set Case Study

2018

Teh. vjesn.

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This paper presents a comparison of the efficacy of unsupervised and supervised discretization methods for educational data from blended learning environment. Naïve Bayes classifier was trained for each discretized data set and comparative analysis of prediction models was conducted. The research goal was to transform numeric features into maximum independent discrete values with minimum loss of information and reduction of classification error. Proposed unsupervised discretization method was based on the histogram distribution and implementation of oversampling technique. The main contribution of this research is improvement of accuracy prediction using the unsupervised discretization method which reduces the effect of ignoring class feature for educational data set.

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Time series adapted supervised fuzzy discretization: an application to ECG signals

Cited by 2 publications

References 41 publications

EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control

EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control

Improvement of the Accuracy of Prediction Using Unsupervised Discretization Method: Educational Data Set Case Study

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