Forest based on Interval Transformation (FIT): A time series classifier with adaptive features. (1st March 2023)

• Record Type: Journal Article
• Authors: Li, Guiling; Xu, Shaolin; Wang, Senzhang; Yu, Philip S.
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Time series classification -- Feature selection -- Random forest -- Interval-based classifier; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118923
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Time series classification (TSC) is an important task in time series data mining and has attracted a lot of research attention. Most TSC algorithms aim to achieve high classification accuracy while reducing the computational complexity. Currently, Time Series Combination of Heterogeneous and Integrated Embedding Forest (TS-CHIEF) is considered to be one of the state-of-the-art TSC algorithms. However, compared with fast algorithms such as Time Series Forest (TSF), TS-CHIEF still has high computation cost. On the premise that the TSF algorithm is fast, we propose a new TSC algorithm, Forest based on Interval Transformation (called FIT), which takes into account both accuracy and efficiency. FIT uses cross-validation to select appropriate transformation series and corresponding interval features, and adaptively converts the interval features of each series in the process of formal training. Subsequently, the transformed feature set is combined with the random forest training FIT model. We evaluate the performance of FIT on 85 UCR time series classification datasets. The experimental results demonstrate that FIT can achieve better accuracy while maintaining high efficiency compared with the state-of-the-art methods. Highlights: A variety of series transformation and interval features to expand feature space. Adaptive selection of transformation series and feature by cross-validation. Forest based on Interval Transformation for time series classification. Experiments on real datasets verify effectiveness of our proposal.