Abstract
Tree-structured methods are considered one of the most powerful tools in classification and regression. Nowadays, Machine Learning for functional data is gaining great attention in different fields. Tree-based models for functional variables are limited; and therefore, there are in a need to propose an efficient classifier in this active area. The current study focuses on the supervised classification of functional data considering functional covariates and multiclass responses. The traditional binary tree approach is extended into a functional framework combining functional tree-based classification (FCT). Two data-driven approaches, including Fourier transformation and functional derivation, are conformed to process data. The conditional recursive partitioning and functional permutation test are employed to search the independence structure between functional inputs. The Bonferroni P-value adjustment and some hyperparameters are utilized to determine the optimal splitting and control tree growth. The obtained results from comprehensive simulation studies implementation are compared favorably with existing methods and showed a good performance in both of computational time and classification accuracy. Additionally, the applied electrocardiogram dataset demonstrates the usefulness and advantage of the proposed FCT method. The current study findings confirm that the proposed method is more reliable and give a high correct classification rate. Generally, this research line is promising and makes the analysis understandable and informative in the medical data classification context.
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Hael, M.A. Unbiased recursive decision tree for supervised functional data classification with applying on electrocardiogram signals. Int J Data Sci Anal 16, 441–454 (2023). https://doi.org/10.1007/s41060-023-00410-y
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DOI: https://doi.org/10.1007/s41060-023-00410-y