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Authors: Ramisetty Kavya ; Jabez Christopher and Subhrakanta Panda

Affiliation: Department of Computer Science and Information Systems, BITS Pilani, Hyderabad Campus, Telangana, India

Keyword(s): Classifiers, Performance Metrics, Multi-criteria Decision-making.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 83-90. DOI: 10.5220/0010774600003116

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Kavya, R.
AU - Christopher, J.
AU - Panda, S.
PY - 2022
SP - 83
EP - 90
DO - 10.5220/0010774600003116
PB - SciTePress

- Science and Technology Publications, Lda.
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