Impact Score 9.24
Kevin Kam Fung Yuen, Senior Lecturer in Analytics, School of Business, Singapore University of Social Sciences, Singapore; (Email: [email protected]).
Jenq-Shiou Leu, Professor and Chairman, Department of Electronic and Computer Engineering(ECE), Graduate Institute of Electro-Optical Engineering (GIEOE), National Taiwan University of Science and Technology, Taiwan; (Email: [email protected] ).
Alessio Ishizaka, Professor and Head of Information Systems, Supply chain and Decisions Making Department, NEOMA Business School, France; (Email: [email protected]).
Hissam Tawfik, Professor of Computer Science (Artificial Intelligence), School of Built Environment, Engineering & Computing, Leeds Beckett University, United Kingdom; (Email: [email protected]).
Frans Coenen, Professor, Department of Computer Science, University of Liverpool, Liverpool, UK; (Email: [email protected] )
Decisions can be made using human judgements, data analytics, or a combination of the two. With the rapid growth of data, various data analytics techniques have been adopted to explore data to find meaningful patterns to support decision making. On the other hand, a lot of decision problems are without past data, or the related data exists but is very difficult and/or expensive to obtain; in which case formulation of a suitable decision model based on ‘expert’ judgements is the main solution for decision making. Whilst many decision problems are supported with partial data or are not merely based on historical data to find patterns, hybrid techniques integrating Expert Decision Models (EDMs) into Data Analytics Algorithms (DAAs) present a promising solution for complex decision and data analytics problems.
Data analytics techniques use modern statistical and machine learning mechanisms to analyze diverse kinds of data, on either a small or big scale, to discover information or knowledge for better decision making. Data analytics techniques may refer to clustering, regression, classification, association learning, reinforcement learning, evolutionary learning, deep learning, or statistical learning.
EDMs are concerned with decision making techniques based on expert judgements, preferences, or opinions as inputs. EDM may refer to the research areas of multi-criteria decision making, recommender systems, user preference engineering, knowledge engineering and expert systems.
This special issue aims to bring together academia and practitioners of both applied decision science and applied data science to report on the recent developments to integrate decision models based on expert judgements into data analytics algorithms to form sophisticated approaches for solving complex decision problems for various application domains.
Relevant applications using Expert Decision Making for Data Analytics include (but are not limited to) the following:
Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at https://ees.elsevier.com/asoc. All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review. Authors should select "VSI: Expert DM" when they reach the "Article Type" step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues.