HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis

Autor:	Keshavarzian, Alireza, Valaee, Shahrokh
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, there has been a notable upsurge in exploring feature extraction through random sampling. Unlike deep convolutional networks, these methods sidestep elaborate training procedures, yet they often necessitate generating a surplus of features to comprehensively encapsulate time series nuances. Consequently, some features may lack relevance to labels or exhibit multi-collinearity with others. In this paper, we propose a novel hierarchical feature selection method aided by ANOVA variance analysis to address this challenge. Through meticulous experimentation, we demonstrate that our method substantially reduces features by over 94% while preserving accuracy -- a significant advancement in the field of time series analysis and feature selection. Comment: 6 pages, 5 figures, IEEE Machine Learning and Signal processing 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.16048 Zobrazit plný text záznamu View this record from Arxiv