Work

E-CLONALG: A classifier based on Clonal Selection Algorithm

Year: 2018

Type: article

Abstract: This paper proposes an improved version of CLONALG, Clone Selection Algorithm based on Artificial Immune System(AIS), that matches with the conventional classifiers in terms of accuracy tested on the same data sets. Clonal Selection Algorithm is an artificial immune system model. Instead of randomly selecting antibodies, it is proposed to take k memory pools consisting of all the learning cases. Also, an array averaged over the pools is created and is considered for cloning. Instead of using the best clone and calculating the similarity measure and comparing with the original cell, here, k best clones were selected, the average similarity measure was evaluated and noise was filtered. This process enhances the accuracy from 76.9 percentage to 94.2 percentage, ahead of the conventional classification methods.

Source: Transactions on Machine Learning and Artificial Intelligence

DOI: 10.14738/tmlai.46.2562

Authors: Rama Krushna Das ., Sanghamitra Dash ., Rabindra Kishore Mishra ., Arijit Panigrahy .

Institutions: National Informatics Centre, Berhampur University

Language: English

Topic: Artificial Immune Systems Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2575883121

FWCI:

Cites: 8

Cited by: 1

Related to: 10