Development and Comparative Analysis of an Instance-Based Machine Learning Classifier

Govind Agarwal, Chirag Goel, Chunduru Abhijit, Alok Chauhan

2023

Abstract

Classification algorithms make it easy to classify many real-world problems, but they come with some cost. The existing classification algorithms have complex architectures, which can sometimes make the classification task tedious. This paper introduces a classification algorithm, which aims to improve upon existing methods by incorporating class count as a target feature. In this study, we attempt to offer a classification method that works with three different categories of datasets, viz., categorical, numerical, and a mixture of categorical and numerical. Firstly, for each input feature attribute, proposed algorithm counts the majority class of the target variable to train the model. Then it determines which class has appeared the most, after computing the majority class for each input characteristic. Final output of the classification algorithm would be the class that showed up the most. If there is a tie in the number of attributes, the class with the greater total count wins. Instance can belong to any class if the total count is also the same. Obviously, any attribute, which has the same count across all classes, is redundant or has no bearing on classification. This classification process is compared against several machine learning methods like KNN, logistic classifier and other models. Experimental results on various benchmark datasets demonstrate that the proposed algorithm is reliable and is promising with respect to several state-of-the-art classification methods in terms of classification accuracy as well as computational efficiency.

Paper Citation

in Harvard Style

Agarwal G., Goel C., Abhijit C. and Chauhan A. (2023). Development and Comparative Analysis of an Instance-Based Machine Learning Classifier. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 434-440. DOI: 10.5220/0012509800003739

in Bibtex Style

@conference{ai4iot23,
author={Govind Agarwal and Chirag Goel and Chunduru Abhijit and Alok Chauhan},
title={Development and Comparative Analysis of an Instance-Based Machine Learning Classifier},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={434-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012509800003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Development and Comparative Analysis of an Instance-Based Machine Learning Classifier
SN - 978-989-758-661-3
AU - Agarwal G.
AU - Goel C.
AU - Abhijit C.
AU - Chauhan A.
PY - 2023
SP - 434
EP - 440
DO - 10.5220/0012509800003739
PB - SciTePress