Special Issue on Machine Learning-based Decision Making
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Decision-making requires the optimal or most satisfactory solution to a decision problem. Classical decision-making is model-based, one example being multi-objective decision-making. For some years, huge amounts of static and streaming data have been generated in daily life by governments, industries and other sources, and decision makers are now acquiring improved abilities to analyze data and form an emerging methodology – data-driven decision-making. This methodology is also called machine learning-based decision-making, since it requires the use of various machine learning methods to learn from data.
Increasingly, developments in machine learning have resulted in an opportunity for intelligent decision support, hence a variety of machine learning-based decision-making methods have been investigated and developed in recent years. Decision-makers and stakeholders must react quickly to insights and gain advantage from these uncertain, dynamic and massive data or data stream environments, so a number of advanced machine learning techniques have recently been devised and applied in decision making, such as transfer-learning based decisionmaking and reinforcement learning-based decision-making. To effectively handle complex decision-making problems and successfully apply machine learning techniques in decision support systems, there is an urgent and strongly need to develop new methodologies and techniques in machine learning-based decision-making.
This special issue is devoted to offer a systematic overview of machine learning-based decision-making, and to develop innovative approaches, models and systems in this emerging field. It will provide a leading forum for disseminating the latest results of theoretical research, technological development, and practical applications in the field. It will attempt to represent an advance in highlighting state-of-the-art of machine learningbased decision-making in Big Data environments. This special issue will therefore collect a set of high-quality, original and innovative research results including (but not limited to) the following topics:
Machine learning-based decision-making/decision support systems
Adaptive data analytics and data-driven forecasting
Machine learning for prediction/decision-making in data streams
Machine learning for prediction/decision-making in complex situations
Machine learning under concept drift
Various machine learning methods for dynamic decision-making
Various machine learning methods in uncertain environments for adaptive decision-making
Data-driven decision-making in a big data environment