Multiple objective metaheuristics for feature selection based on stakeholder requirements in credit scoring

Simumba, Naomi; Okami, Suguru; Kodaka, Akira; Kohtake, Naohiko

doi:10.1016/j.dss.2021.113714

Cited by 20 publications

(4 citation statements)

References 18 publications

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“…Topic 8- “Data Mining and Machine Learning for Decision Making” concerns the key research that exploits soft computing approaches in various business decision-making contexts. There are ample studies that apply machine learning approaches to business problems such as credit rating (He et al ., 2022), credit scoring (Simumba et al ., 2022), employee churn prediction (Pekel Ozmen and Ozcan, 2022), financial distress prediction (Wu et al ., 2022a, b, c, d), customer churn management (Kozak et al ., 2021), hedge fund return prediction (Wu et al. , 2021b) and bankruptcy prediction (du Jardin, 2021).…”

Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…Consequently, government policies that drive continuous improvement can enable the organization to satisfy a wide range of market and social requirements and thereby play a significant role in business success (Hariyani and Mishra, 2022). Also, other research scholars have recognized the high significance of stakeholder participation in implementing continuous improvement strategies for quality management and other organizational change benefits (Ratanaburi et al ., 2021; Simumba et al ., 2022). Past published studies affirm the criticality of the competitive business environment in supporting the implementation of supply chain innovations for organizational change (Aqlan and Al-Fandi, 2018; Buitrago and Camargo, 2021; Soltani et al ., 2021).…”

Section: Discussionmentioning

confidence: 99%

Organizational change towards Lean Six Sigma implementation in the manufacturing supply chain: an integrated approach

Orji

U-Dominic

2022

BPMJ

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PurposeThe topic of Lean Six Sigma (LSS) implementation in a supply chain context is an emerging research stream comprising of diverse objectives and complex processes thereby presenting opportunities for further exploration and organizational process improvement. Thus, this study proposes an integrated multi-criteria decision-making methodology to determine what can facilitate the successful implementation of LSS as an organizational change strategy in the manufacturing supply chain.Design/methodology/approachThe proposed methodology based on Decision-Making Trial and Evaluation Laboratory and hierarchical Evaluation Based on Distance to Average Solution is employed to ascertain the relative importance and priorities of an identified framework of factors with the aid of opinions of managers in the Nigerian plastics industry.FindingsThe results show a high significance of institution-based factors (e.g. government regulations) and present relevant implications to the policymakers as well as the managers and practitioners of the plastics manufacturing industry.Originality/valueThis study indicates a possible pathway to accurately evaluate a framework of critical factors to integrate and institutionalize LSS in the manufacturing supply chain for organizational performance improvement.

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“…In the current research work, the GOA adjusts the hyperparameter values of the DL models. GOA is a bio-inspired optimization algorithm that simulates the swarming performance of grasshoppers in nature [29]. It drives by simulating the interaction and the movement of grasshoppers from a population to search for better performance.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Ragab,

Abdushkour,

Maghrabi

et al. 2023

Sustainability

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Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

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Multiple objective metaheuristics for feature selection based on stakeholder requirements in credit scoring

Cited by 20 publications

References 18 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

Organizational change towards Lean Six Sigma implementation in the manufacturing supply chain: an integrated approach

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

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