Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming

Chan, Kit Yan; Kwong, C. K.; Jiang, Huimin

doi:10.1016/j.engappai.2021.104442

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The data gathered from the experts were used as the inputs. The size of data was considerably prudent when compared to the size of data in other related studies (Chan et al 2021;Dahiru et al 2021;Fallahpour et al 2021;Quintero-Domínguez et al 2021).…”

Section: Genetic Programmingmentioning

confidence: 97%

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

Islam

Mohandes

Mahdiyar

et al. 2022

J. Constr. Eng. Manage.

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Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, the thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a Hybrid Predictive-Probabilistic-based Model (HPPM) that integrates a genetic programming technique with a Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation.The simulation outcomes show that 40.48% of a project's initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75% with 90% of confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants' budget accuracy not only at the initial project delivery phase but throughout the project lifecycle.Theoretically, the HPPM model could be employed for cost overrun prediction in other types of power plant projects.

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Section: Genetic Programmingmentioning

confidence: 97%

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

Islam

Mohandes

Mahdiyar

et al. 2022

J. Constr. Eng. Manage.

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show abstract

“…The obtained fashion style models can potentially help marketing and product design specialists better understand customer preferences in the ecommerce fashion industry. Chan et al [35] have formulated a multi-objective optimization problem which attempts to equally learn imbalances online review data collected from popular and unpopular products in marketplace and addressed the problem of how to predict customer satisfaction with product design attributes through online data imbalance in order to improve the success of the product in the marketplace. Joung and Kim [20] proposed to identify product attributes from online reviews using an approach based on latent Dirichlet assignment and the case study of Android smartphones demonstrates that the proposed method yields better LDA results to identify product attributes than previous methods.…”

Section: Analysis Of Customer Requirementsmentioning

confidence: 99%

A dynamic customer requirement mining method for continuous product improvement

Zhao

Guo

et al. 2022

Auton. Intell. Syst.

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The key to successful product development is better understanding of customer requirements and efficiently identifying the product attributes. In recent years, a growing number of researchers have studied the mining of customer requirements and preferences from online reviews. However, since customer requirements often change dynamically on multi-generation products, most existing studies failed to discover the correlations between customer satisfaction and continuous product improvement. In this work, we propose a novel dynamic customer requirement mining method to analyze the dynamic changes of customer satisfaction of product attributes based on sentiment and attention expressed in online reviews, aiming to better meet customer requirements and provide the direction and content of future product improvement. Specifically, this method is divided into three parts. Firstly, text mining is adopted to collect online review data of multi-generation products and identify product attributes. Secondly, the attention and sentiment scores of product attributes are calculated with a natural language processing tool, and further integrated into the corresponding satisfaction scores. Finally, the improvement direction for next-generation products is determined based on the changing satisfaction scores of multi-generation product attributes. In addition, a case study on multi-generation phone products based on online reviews was conducted to illustrate the effectiveness and practicality of the proposed methodology. Our research completes the field of requirements analysis and provides a new dynamic approach to requirements analysis for continuous improvement of multi-generation products, which can help enterprises to accurately understand customer requirements and improve the effectiveness and efficiency of continuous product improvement.

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SWSEL: Sliding Window-based Selective Ensemble Learning for class-imbalance problems

Dai

Liu

Yang

2023

Engineering Applications of Artificial Intelligence

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Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming

Cited by 6 publications

References 38 publications

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

A dynamic customer requirement mining method for continuous product improvement

SWSEL: Sliding Window-based Selective Ensemble Learning for class-imbalance problems

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