Interactive Machine Learning Approach for Staff Selection Using Genetic Algorithm

Abstract

AbstractMachine learning is meant to extract knowledge from vast data. It is believed to retrieve useful data from enormous raw data. It is applied in several fields in which if there is a big dataset which consists of information about a person’s name, age, address, height, weight, medical test details etc.,. Statistics department may be interested to get the attributes such as name, age and address. Health department may be interested in medical details. They may need to get the ratio of old aged people and wanted to give vaccination to prevent Covid-19. From these raw data, they predict the percentage of Old aged people and the exact number of people who needs more attention. Mere dependence on machine models for benevolence sometimes leads to huge malfunction or loss. So it is advisable to supervise the machine models by having check points. Interaction with the machine is cumbersome and it is fully dependent on the context of the model. In this paper, a different approach is presented for employee work planning schedule. In a moderate firm, employee plan schedule on everyday basis requires a machine model. Genetic Algorithm is used for staff planning and interactive machine learning approach is used for supervision. Human supervision reduces the cost and the algorithm converges rapidly.KeywordsGenetic algorithmOptimizationFitness functionInteractive machine learningCrossoverMutation