ScaPMI: Scaling Parameter for Metric Importance

Ramisetty Kavya, Jabez Christopher, Subhrakanta Panda

2022

Abstract

Selection of an optimal classifier is an important task in supervised machine learning, and it depends on performance analytics, metric-importance, and domain requirements. This work considers distinct classifiers as decision alternatives and various performance metrics as decision criteria. The weight for each metric is computed by applying an Analytic hierarchy process on the proposed scaling parameter. Multi-criteria decision-making methods consider the performance of classifiers along with metric-weights to generate the ranking order of alternatives. Some typical experimental observations: Random forest is chosen as an optimal classifier by five MCDM methods for liver disorders dataset; Logistic regression, seems optimal for four MCDM methods over hepatitis dataset, and to three methods over heart disease dataset; many such observations discussed in this work may enable developers to choose appropriate classifier for supervised learning systems.

Paper Citation

in Harvard Style

Kavya R., Christopher J. and Panda S. (2022). ScaPMI: Scaling Parameter for Metric Importance. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 83-90. DOI: 10.5220/0010774600003116

in Bibtex Style

@conference{icaart22,
author={Ramisetty Kavya and Jabez Christopher and Subhrakanta Panda},
title={ScaPMI: Scaling Parameter for Metric Importance},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={83-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010774600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - ScaPMI: Scaling Parameter for Metric Importance
SN - 978-989-758-547-0
AU - Kavya R.
AU - Christopher J.
AU - Panda S.
PY - 2022
SP - 83
EP - 90
DO - 10.5220/0010774600003116