期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：364-370
出版社：Science and Information Society (SAI)
摘要：Artificial Immune-based algorithm is inspired by
the biological immune system as computational intelligence
approach in data analysis. Negative selection algorithm is derived
from immune-based algorithm’s family that used to recognize the
pattern’s changes perform by the gene detectors in
complementary state. Due to the self-recognition ability, this
algorithm is widely used to recognize the abnormal data or nonself
especially for fault diagnosis, pattern recognition, network
security etc. In this study, the self-recognition performance
proposed by the negative selection algorithm been considered as
a potential technique in classifying employee’s competency.
Assessing the employee’s performance in organization is an
important task for human resource management people to
identify the right candidate in job promotion assessment. Thus,
this study attempts to propose an immune-based model in
assessing academic leadership performance. There are three
phases involved in experimental phase i.e. data acquisition and
preparation; model development; and analysis and evaluation.
The data consists of academic leadership proficiency was
prepared as data-set for learning and detection processes.
Several experiments were conducted using cross validation
process on different model to identify the most accurate model.
Therefore, the accuracy of NS classifier is considered acceptable
enough for this academic leadership assessment case study. For
enhancement, other immune-based algorithm or bio-inspired
algorithms, such as genetic algorithm, particle swam
optimization, ant colony optimization would also be considered
as a potential algorithm for performance assessment.
