Business analytics use techniques from data science, data mining, artificial intelligence (especially, machine learning), mathematics and statistics to gain insights and understanding on the performance of business processes. The gained insights and knowledge help driving the business planning. As employees play important roles in the business process, having a tool to classify and predict their wage levels is desirable. Such classification and prediction enables the public or private sector to offer competitive wages for recruiting and retaining employees. In this paper, we present a tool for classifying and predicting wage levels. It incorporates fuzzy logic into a machine-learning tool to support business analytics on big data. Evaluation results show the applicability of our tool for classification and prediction of wages levels in the business world, which in turn supports business analytics in complex artificial intelligence environments.

A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments

Grasso G. M.;
2019-01-01

Abstract

Business analytics use techniques from data science, data mining, artificial intelligence (especially, machine learning), mathematics and statistics to gain insights and understanding on the performance of business processes. The gained insights and knowledge help driving the business planning. As employees play important roles in the business process, having a tool to classify and predict their wage levels is desirable. Such classification and prediction enables the public or private sector to offer competitive wages for recruiting and retaining employees. In this paper, we present a tool for classifying and predicting wage levels. It incorporates fuzzy logic into a machine-learning tool to support business analytics on big data. Evaluation results show the applicability of our tool for classification and prediction of wages levels in the business world, which in turn supports business analytics in complex artificial intelligence environments.
2019
978-1-5386-1728-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3164480
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