Machine learning is the branch of artificial intelligence whose
goal is to develop algorithms that add learning capabilities to
computers. Ensembles are an integral part of machine learning. A
typical ensemble includes several algorithms performing the task of
prediction of the class label or the degree of class membership for
a given input presented as a set of measurable characteristics,
often called features. Feature Selection and Ensemble Methods for
Bioinformatics: Algorithmic Classification and Implementations
offers a unique perspective on machine learning aspects of
microarray gene expression based cancer classification. This
multidisciplinary text is at the intersection of computer science
and biology and, as a result, can be used as a reference book by
researchers and students from both fields. Each chapter describes
the process of algorithm design from beginning to end and aims to
inform readers of best practices for use in their own research.
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