TY - BOOK ID - 135110249 TI - Algorithms in Decision Support Systems PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - semi-supervised learning KW - transfer learning KW - radar emitter KW - decision support systems KW - population health management KW - big data KW - machine learning KW - deep learning KW - personalized patient care KW - Nonlinear regression KW - interactive platform KW - component-based approach KW - software architecture KW - Eclipse-RCP (Rich Client Platform) KW - spatial prediction KW - rule-based expert systems KW - tennis hitting technique KW - computer algebra systems KW - Groebner bases KW - Boolean logic KW - data envelopment analysis KW - dimensionality reduction KW - ensembles KW - exhaustive state space search KW - entropy KW - associative classification KW - class association rule KW - vertical data representation KW - classification KW - algorithm evaluation KW - parallel algorithms KW - multi-objective optimization KW - train rescheduling KW - very large-scale decision support systems KW - very large-scale data and program cores of information systems KW - meta-database KW - teleological meta-database KW - thematic list KW - indicators list KW - computational methods list KW - geographically dispersed systems KW - external sources UR - https://www.unicat.be/uniCat?func=search&query=sysid:135110249 AB - This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book. ER -