Optimizing Recruitment Process within Businesses: Predicting Interview Attendance Using C4. 5 Algorithm
Version: 1,
Uploaded by: Administrator,
Date Uploaded:
26 November 2022
Warning
You are about to be redirected to a website not operated by the Mauritius Research and Innovation Council. Kindly note that we are not responsible for the availability or content of the linked site. Are you sure you want to leave this page?
An essential component of the job recruitment process involves conducting interviews, during which decision is made on the appropriateness of a candidate for a particular job. An important challenge faced by interviewers during the recruitment process relates to determining whether or not selected candidates will attend the job interview. Candidates failing to attend interviews because of various reasons lead to time wasting and as such, effective prediction is needed by human resource managers about whether or not a candidate will attend an interview. In other words, if the human resource department knows exactly whether or not the job seeker is attending the interview, a more efficient interview invitation strategy could be worked upon. However, even though different machine learning techniques are available for prediction, limited work has been undertaken to address this recruitment process related issue. Taking cognizance of this problem, this paper investigates, analyzes, and predicts candidate attendance during interviews in order to optimize recruitment process within businesses. For this, C4.5 algorithm was applied on an open dataset related to job interview attendance. Following generation of a classification tree, results showed some insightful patterns on when candidates do not attend job interviews.