Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Analysis of Variable Importance Measurement Techniques for Classification of Road Surfaces

Authors
Anupama Jawale1, *, Ganesh Magar1
1Post Graduate Department of Computer Science, SNDT Women’s University, Mumbai, 400049, India
*Corresponding author. Email: anupama.jawale26@gmail.com
Corresponding Author
Anupama Jawale
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_40How to use a DOI?
Keywords
Classification; Decision Trees; Regression; Variable Importance
Abstract

The term variable importance refers to the role of an attribute in making accurate predictions. A particular model, when relies majorly on multiple variables, increases variable importance of those variables in positive direction. Variable importance is applied to various classification and regression models using different methods. For example, in regression model, higher value Root Mean Squared Error (RMSE) is the indicator of high importance to that variable, whereas in classification model, higher number of splits associated with a variable determines its importance in the model. In this research study, we have considered a problem of road surface classification depending upon 17 variables associated with vehicle parameters. This is a multiclass classification problem. Different classification and regression models are used, and variable importance of each model is evaluated on the metrics like RMSE, Goodness of fit model. Outcome of this research study shows all models define a common set of 5 to 7 higher importance variable rankings to predict dependant variable.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-196-8_40
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_40How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Anupama Jawale
AU  - Ganesh Magar
PY  - 2023
DA  - 2023/08/10
TI  - Analysis of Variable Importance Measurement Techniques for Classification of Road Surfaces
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 521
EP  - 537
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_40
DO  - 10.2991/978-94-6463-196-8_40
ID  - Jawale2023
ER  -