A unified, coherent, and expansive treatment of current classifier ensemble methods
Mail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are just a few of the areas requiring reliable, precise pattern recognition.
Although in the past, pattern recognition has focused on designing single classifiers, recently the focus has been on combining several classifiers and getting a consensus of results for greater accuracy. This interest in combining classifiers has grown astronomically in recent years, evolving into a rich and dynamic, if loosely structured, discipline. Combining Pattern Classifiers: Methods and Algorithms represents the first attempt to provide a comprehensive survey of this fast-growing field. In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art.
Covering a broad range of methodologies, algorithms, and theories, the text addresses such questions as:
- Why should we combine classifiers?
- What are the current approaches for building classifier ensembles?
- What fusion methods can we use?
- How do we measure diversity in a classifier ensemble and is diversity really a key factor to its success?
Replete with case studies and real-world applications, this groundbreaking text will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones.
