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001 978-3-319-97919-9
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008 181129s2019 sz | s |||| 0|eng d
020 _a9783319979199
_9978-3-319-97919-9
024 7 _a10.1007/978-3-319-97919-9
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aWan, Cen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aHierarchical Feature Selection for Knowledge Discovery
_h[electronic resource] :
_bApplication of Data Mining to the Biology of Ageing /
_cby Cen Wan.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIV, 120 p. 52 illus., 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing,
_x2197-8441
505 0 _aIntroduction -- Data Mining Tasks and Paradigms -- Feature Selection Paradigms -- Background on Biology of Ageing and Bioinformatics -- Lazy Hierarchical Feature Selection -- Eager Hierarchical Feature Selection -- Comparison of Lazy and Eager Hierarchical Feature Selection Methods and Biological Interpretation on Frequently Selected Gene Ontology Terms Relevant to the Biology of Ageing -- Conclusions and Research Directions.
520 _aThis 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 providesthe 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.
650 0 _aData mining.
650 0 _aBioinformatics.
650 0 _aArtificial intelligence.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputational and Systems Biology.
650 2 4 _aArtificial Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319979182
776 0 8 _iPrinted edition:
_z9783319979205
830 0 _aAdvanced Information and Knowledge Processing,
_x2197-8441
856 4 0 _uhttps://doi.org/10.1007/978-3-319-97919-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175085
_d175085