Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees

• URL: http://arxiv.org/abs/2202.03577v1
• Date: Wed, 2 Feb 2022 03:49:01 GMT
• Title: Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees
• Authors: Gopal Nath, Antoine Harfouche, Austin Coursey, Krishna K. Saha, Srikanth Prabhu, Saptarshi Sengupta
• Abstract summary: Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. This study is to develop a decision support tool to predict absenteeism among potential employees.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose - Inefficient hiring may result in lower productivity and higher training costs. Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. Also, employers typically spend a considerable amount of time managing employees who perform poorly. The purpose of this study is to develop a decision support tool to predict absenteeism among potential employees. Design/methodology/approach - We utilized a popular open-access dataset. In order to categorize absenteeism classes, the data have been preprocessed, and four methods of machine learning classification have been applied: Multinomial Logistic Regression (MLR), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selected the best model, based on several validation scores, and compared its performance against the existing model; we then integrated the best model into our proposed web-based for hiring managers. Findings - A web-based decision tool allows hiring managers to make more informed decisions before hiring a potential employee, thus reducing time, financial loss and reducing the probability of economic insolvency. Originality/value - In this paper, we propose a model that is trained based on attributes that can be collected during the hiring process. Furthermore, hiring managers may lack experience in machine learning or do not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool that can be used without prior knowledge of machine learning algorithms.

Related papers

• Employee Turnover Analysis Using Machine Learning Algorithms [0.0]
  Machine learning techniques can be used to monitor and mitigate employee turnover risk. Three different supervised learning algorithms are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.
  arXiv  Detail & Related papers  (2024-02-06T11:17:16Z)
• Fast Model Debias with Machine Unlearning [54.32026474971696]
  Deep neural networks might behave in a biased manner in many real-world scenarios. Existing debiasing methods suffer from high costs in bias labeling or model re-training. We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
  arXiv  Detail & Related papers  (2023-10-19T08:10:57Z)
• PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace [1.4364491422470593]
  We propose a blockchain-based marketplace called "PredictChain" for predictive machine-learning models. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models.
  arXiv  Detail & Related papers  (2023-07-27T19:56:18Z)
• Retention Is All You Need [9.570332155350055]
  We propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System. The system is designed to assist HR departments in interpreting the predictions provided by machine learning models.
  arXiv  Detail & Related papers  (2023-04-06T14:29:20Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)
• Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning [65.268245109828]
  In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models. Deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning.
  arXiv  Detail & Related papers  (2022-02-22T02:33:54Z)
• Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support [7.55043162959755]
  We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud. We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features.
  arXiv  Detail & Related papers  (2020-12-14T22:35:02Z)
• FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment [79.23531577235887]
  This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data. Aditionally, the demo includes a new algorithm for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.
  arXiv  Detail & Related papers  (2020-09-12T17:45:09Z)
• Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty [1.452875650827562]
  This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.
  arXiv  Detail & Related papers  (2020-08-27T11:49:25Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.