Meta-learning for the selection of algorithms and features for time series classification

• Grant number: 22/00302-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: April 01, 2022
• End date: May 28, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Diego Furtado Silva
• Grantee: Anderson Henrique Giacomini
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated scholarship(s): 22/12498-2 - Automatically weighting ensemble-based time series classification algorithms, BE.EP.IC

Abstract

Due to the increasing collection of overtime observed data, time series are becoming a ubiquitous type of data in human beings' daily lives. This fact has also caused a significant increase in the number of techniques for time series classification in recent decades. In this scenario, when a researcher or developer needs a solution based on a classification model, there is a wide range of algorithms options to apply. When only one algorithm is chosen, its performance may not achieve satisfactory results for the problem to be solved. Even sets-based methods, which attempt to reduce the impacts of a bad algorithm choice, may fail on specific data sets. Therefore, the safest alternative is to perform cross-validation procedures to guide the classification algorithm choice. This approach usually guarantees a good choice, but it is very computationally expensive. In this context, we propose the creation of meta-learning-based recommendation models for time series classification algorithms. The idea behind this proposal is to describe previously analyzed problems (datasets) in order to efficiently induce meta-models through Machine Learning algorithms. These meta-models will be used to recommend classification algorithms for new datasets in order to avoid a very costly selection procedure. The Meta-learning will be evaluated at two levels: selection of a single classification algorithm and selection of parameters to compose an algorithm based on feature extraction through random convolutional kernels.(AU)