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Hierarchical Feature Selection for Knowledge Discovery - Application of Data Mining to the Biology of Ageing (Hardcover, 1st ed. 2019)
Loot Price: R2,773
Discovery Miles 27 730
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Hierarchical Feature Selection for Knowledge Discovery - Application of Data Mining to the Biology of Ageing (Hardcover, 1st ed. 2019)
Series: Advanced Information and Knowledge Processing
Expected to ship within 10 - 15 working days
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This book is the first work that systematically describes the
procedure of data mining and knowledge discovery on Bioinformatics
databases by using the state-of-the-art hierarchical feature
selection algorithms. The novelties of this book are three-fold. To
begin with, this book discusses the hierarchical feature selection
in depth, which is generally a novel research area in Data
Mining/Machine Learning. Seven different state-of-the-art
hierarchical feature selection algorithms are discussed and
evaluated by working with four types of interpretable
classification algorithms (i.e. three types of Bayesian network
classification algorithms and the k-nearest neighbours
classification algorithm). Moreover, this book discusses the
application of those hierarchical feature selection algorithms on
the well-known Gene Ontology database, where the entries (terms)
are hierarchically structured. Gene Ontology database that unifies
the representations of gene and gene products annotation provides
the resource for mining valuable knowledge about certain biological
research topics, such as the Biology of Ageing. Furthermore, this
book discusses the mined biological patterns by the hierarchical
feature selection algorithms relevant to the ageing-associated
genes. Those patterns reveal the potential ageing-associated
factors that inspire future research directions for the Biology of
Ageing research.
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