Advances in Combining Intelligent Methods

Complex problems usually cannot be solved by individual methods or techniques and require the synergism of more than one of them to be solved. This book presents a number of current efforts that use combinations of methods or techniques to solve complex problems in the areas of sentiment analysis, search in GIS, graph-based social networking, intelligent e-learning systems, data mining and recommendation systems. Most of them are connected with specific applications, whereas the rest are combinations based on principles. Most of the chapters are extended versions of the corresponding papers presented in CIMA-15 Workshop, which took place in conjunction with IEEE ICTAI-15, in November 2015. The rest are invited papers that responded to special call for papers for the book. The book is addressed to researchers and practitioners from academia or industry, who are interested in using combined methods in solving complex problems in the above areas.

Involves the combination of intelligent methods either based on first principles or in the context of specific applications Includes both theory and practical applications Presents approaches covering different intelligent methods and technologies, such as model-based diagnosis, linear optimization, probabilistic reasoning, uncertain reasoning, clustering methods, neuro-symbolic representation, pattern recognition etc Includes supplementary material: sn.pub/extras

Contenu
Real-Time Investors' Sentiment Analysis from Newspaper Article.- On the effect of adding nodes to TSP instances: An empirical analysis.- Comparing Algorithmic Principles for Fuzzy Graph Communities over Neo4j.- Difficulty estimation of exercises on tree-based search algorithms using neuro-fuzzy and neuro-symbolic approaches.- Generation and Nonlinear Mapping of Reducts - Nearest Neighbor Classification.- New quality indexes for optimal clustering model identification based on cross-domain approach.- A hybrid user and item based collaborative filtering approach by possibilistic similarity fusion.-