Symbolic representation based on trend features for biomedical data classification

Abstract

BACKGROUND: The widespread access to portable medical devices or new personal devices is boosting the amount of biomedical data. These devices provide a growing massive data that far exceeds the analytical ability of a professional doctor. The computer-assisted analysis of biomedical data has become an essential tool in medicine diagnosis.

OBJECTIVE: Due to the advantages of discrete, noise elimination and dimensionality reduction, symbolic representation of biomedical data has attracted great interest. The symbolization results provide efficiently performing at data mining, such as pattern discovery, anomaly detection and association rules mining, so we want to use the method to improving the biomedical data classification.

METHODS: In this paper, we introduce a novel symbolic representation method, called Trend Feature Symbolic Approximation (TFSA).

RESULTS: The TFSA focuses on retaining most of the original series' trend features, and it also very suitable for subsequent mining work, such as association rules mining.

CONCLUSION: The TFSA provides the lower bounding guarantee and the experimental results show that comparing with some existing methods, its classification accuracy is improved.