Search

Multilevel decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a multiple level hierarchy. Significant efforts have been devoted to understanding the fundamental concepts and developing diverse solution algorithms associated with multilevel decision-making by researchers in areas of both mathematics/computer science and business areas. Researchers have emphasized the importance of developing a range of multilevel decision-making techniques to handle a wide variety of management and optimization problems in real-world applications, and have successfully gained experience in this area. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but also the practical developments in multilevel decision-making in business. This paper systematically reviews up-to-date multilevel decision-making techniques and clusters related technique developments into four main categories: bi-level decision-making (including multi-objective and multi-follower situations), tri-level decision-making, fuzzy multilevel decision-making, and the applications of these techniques in different domains. By providing state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in theoretical research results and applications in relation to multilevel decision-making techniques. [ABSTRACT FROM AUTHOR]

Learning a distance metric from training samples is often a crucial step in machine learning and pattern recognition. Locality, compactness and consistency are considered as the key principles in distance metric learning. However, the existing metric learning methods just consider one or two of them. In this paper, we develop a multi-granularity distance learning technique. First, a new index, neighborhood granule margin, which simultaneously considers locality, compactness and consistency of neighborhood, is introduced to evaluate a distance metric. By maximizing neighborhood granule margin, we formulate the distance metric learning problem as a sample pair classification problem, which can be solved by standard support vector machine solvers. Then a set of distance metrics are learned in different granular spaces. The weights of the granular spaces are learned through optimizing the margin distribution. Finally, the decisions from different granular spaces are combined with weighted voting. Experiments on UCI datasets, gender classification and object categorization tasks show that the proposed method is superior to the state-of-the-art distance metric learning algorithms. [ABSTRACT FROM AUTHOR]