Due to increasing demands for dimensionality reduction, research on
feature selection has deeply and widely expanded into many fields,
including computational statistics, pattern recognition, machine
learning, data mining, and knowledge discovery. Highlighting
current research issues, Computational Methods of Feature Selection
introduces the basic concepts and principles, state-of-the-art
algorithms, and novel applications of this tool.
The book begins by exploring unsupervised, randomized, and
causal feature selection. It then reports on some recent results of
empowering feature selection, including active feature selection,
decision-border estimate, the use of ensembles with independent
probes, and incremental feature selection. This is followed by
discussions of weighting and local methods, such as the ReliefF
family, "k"-means clustering, local feature relevance, and a new
interpretation of Relief. The book subsequently covers text
classification, a new feature selection score, and both
constraint-guided and aggressive feature selection. The final
section examines applications of feature selection in
bioinformatics, including feature construction as well as
redundancy-, ensemble-, and penalty-based feature selection.
Through a clear, concise, and coherent presentation of topics,
this volume systematically covers the key concepts, underlying
principles, and inventive applications of feature selection,
illustrating how this powerful tool can efficiently harness
massive, high-dimensional data and turn it into valuable, reliable
information.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
