Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination

From the book reviews:

“The author presents an up-to-date review of recent advances in this area. … this is a very interesting, complete, and up-to-date book about various aspects of machine learning and decision making using hybrid classifiers. Although the author makes this book accessible to students and practitioners, it is probably more oriented to advanced undergraduate or graduate courses focused on improving machine learning methods and applications.” (Fernando Osorio, Computing Reviews, July, 2014)

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.