Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Chapter

Download BibTeX

Title

Comparision of Models Built Using AutoML and Data Fusion

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ D ] phd student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • Automated machine learning
  • AutoML tools
  • Auto-sklearn
  • Hyperparameter optimization
  • Data fusion
  • Combination of interpretation
  • Prediction models
Abstract

EN Automated machine learning (AutoML) has made life easier for data analysts or scientists by providing quick insights into data by building machine learning (ML) models. AutoML techniques are applied to vast areas from image processing, speech recognition, natural language processing reinforcement learning, and more. However, there is still room for many improvements. AutoML techniques focus only on problems related to predictive modeling, and most of them are designed to work with structured data. AutoML techniques are also time-consuming as they require time to select the appropriate ML pipeline. This paper presents an alternative time-efficient approach for mixed data (both categorical and numerical features obtained from UCI and Kaggle repository) using a data fusion process, which provides high macro average accuracy in less time as compared to AutoML. The AutoML tool considered here is autoscikit-learn (auto-sklearn). This specific library is built in Python using scikit-learn. The implementation of data fusion is also done in Python using scikit-learn. We conclude from the experimental analysis that the pipeline constructed provides better results than the auto-sklearn. This obtained conclusion is supported by a statistical test (Wilcoxon signed ranks test) based on macro average accuracy obtained for both approaches.

Date of online publication

29.08.2022

Pages (from - to)

301 - 314

DOI

10.1007/978-3-031-15740-0_22

URL

https://link.springer.com/chapter/10.1007/978-3-031-15740-0_22

Book

Advances in Databases and Information Systems : 26th European Conference, ADBIS 2022, Turin, Italy, September 5–8, 2022, Proceedings

Presented on

26th European Conference on Advances in Databases and Information Systems ADBIS 2022, 5-8.09.2022, Turin, Italy

Ministry points / chapter

20

Ministry points / conference (CORE)

70

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.