Titlebook: Instance Selection and Construction for Data Mining; Huan Liu,Hiroshi Motoda Book 2001 Springer Science+Business Media Dordrecht 2001 DOM.

pester

Flustered

Progressive Samplingely larger samples as long as model accuracy improves. We explore several notions of efficient progressive sampling, including both methods that are asymptotically optimal and those that take into account prior expectations of appropriate data size. We then show empirically that progressive sampling indeed can be remarkably efficient.

Migratory

Learning via Prototype Generation and Filtering the problem. We compare different variants of integration as well as existing learning algorithms such as C4.5 and KNN. Our new framework shows good performance in data reduction while maintaining or even improving classification accuracy in 19 real data sets.

意外

狼群

insomnia

文艺

A Unifying View on Instance Selectioniscuss its strengths and weaknesses. Then, we propose an enhanced framework for instance selection, generic sampling, and summarize evaluation results for several instantiations of its implementation. Finally, we conclude with open issues and research challenges for instance selection as well as focusing in general.

放牧

vitrectomy

The Landmark Model: An Instance Selection Method for Time Series Datats of features of landmarks, we can efficiently compute different similarity measures that are invariant under corresponding subsets of six transformations. We discuss a generalized approach for removing noise from raw time series without smoothing out the peaks and bottoms, and present a pattern representation based on the Landmark Model.

GRIEF