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A Family Review of Parameter-Learning Models and Algorithms for Making Actionable Decisions

A Family Review of Parameter-Learning Models and Algorithms for Making Actionable Decisions
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Author(s): Chun-Kit Ngan (The Pennsylvania State University, USA)
Copyright: 2019
Pages: 14
Source title: Advanced Methodologies and Technologies in Business Operations and Management
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-7362-3.ch051

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Abstract

The authors describe and explain a family development of the parameter-learning models and algorithms: expert query parametric estimation (EQPE)-based models and checkpoint-oriented algorithms. This class of models and algorithms combines the strength of both qualitative and quantitative methodologies to complement each other to learn optimal decision parameters in an efficient manner to make actionable recommendations. More specifically, this family of models and algorithms relies on domain expertise to select attributes and conditions against the data, from which the family of EQPE-based models and checkpoint-oriented algorithms can learn decision parameters efficiently. To demonstrate the effectiveness and the efficiency of the models and algorithms, the authors have conducted two experimental case studies (i.e., the stock market and the glycemia, respectively). The authors show that their approaches are more effective and produce results that are superior to those of the two other approaches mentioned above.

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