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 over time observed data, time series are becoming a ubiquitous type of data in the human beings daily lives. This fact has also caused a significant increase in the number of techniques for time series classification in the 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.