Artificial Immune Clonal Selection Based Algorithm in Academic Talent Selection
DOI:
https://doi.org/10.26713/jims.v8i4.554Keywords:
Artificial Immune System, Clonal Selection Algorithms. Talent SelectionAbstract
Artificial immune clonal selection based algorithms are centered on prominent immunological theory as computation mechanisms that emulate processes in biological immune system. Due to their abilities in achieving higher probability for global optimization, this technique has been used in many fields such as machine learning, computer security, pattern recognition, scheduling and etc. This article attempts to study the potential of clonal selection based algorithms for talent selection in higher learning institution. This study consists of three phases; data preparation for pre-processing academic talent data-sets; model construction using four selected artificial immune clonal selection based algorithms; and model analysis to measure the accuracy of the model proposed by the algorithms based on 10-fold cross validation. There were several experiments carried out using different set of training and testing data-sets; the significance test was also conducted in order to support the findings. As a result, Clonal Selection Classifier (CSCA) and CLONal Selection ALGorithm (CLONALG) algorithms proposed slightly high accuracy and would be considered as the potential classifiers for talent selection. For future work, the proposed model can be strengthened by the comparative study with other heuristic based searching algorithms in evolutionary computation paradigms such as genetic algorithm, ant colony optimization, swam algorithms and etc.
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