Negative Selection in Classification using DBLOSUM Matrices as Affinity Function

Adil Ibrahim, Nicholas Taylor

2022

Abstract

This paper presents a novel affinity function for the Negative Selection based algorithm in binary classification. The proposed method and its classification performance are compared to several classifiers using different datasets. One of the binary classification problems includes medical testing to determine if a patient has a particular disease or not. The DBLOSUM in Negative Selection classifier appears to be best suited to classification tasks where false negatives pose a major risk, such as in medical screening and diagnosis. It is more likely than most techniques to result in false positives, but it is as accurate, if not more accurate than most other techniques.

Paper Citation

in Harvard Style

Ibrahim A. and Taylor N. (2022). Negative Selection in Classification using DBLOSUM Matrices as Affinity Function. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 55-62. DOI: 10.5220/0010771100003116

in Bibtex Style

@conference{icaart22,
author={Adil Ibrahim and Nicholas Taylor},
title={Negative Selection in Classification using DBLOSUM Matrices as Affinity Function},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={55-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010771100003116},
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 - Negative Selection in Classification using DBLOSUM Matrices as Affinity Function
SN - 978-989-758-547-0
AU - Ibrahim A.
AU - Taylor N.
PY - 2022
SP - 55
EP - 62
DO - 10.5220/0010771100003116