EAN: 9784431555698

SPRINGERBRIEFS IN STATISTICS STATISTICAL METH...

This book presents a fresh  new approach in that it provides a comprehensive recent review of
challenging problems caused by imbalanced data in prediction and classification  and also in
that it introduces several of the latest statistical methods of dealing with these problems.
The book discusses the property of the imbalance of data from two points of view. The first is
quantitative imbalance  meaning that the sample size in one population highly outnumbers that
in another population. It includes presence-only data as an extreme case  where the presence of
a species is confirmed  whereas the information on its absence is uncertain  which is
especially common in ecology in predicting habitat distribution. The second is qualitative
imbalance  meaning that the data distribution of one population can be well specified whereas
that of the other one shows a highly heterogeneous property. A typical case is the existence of
outliers commonly observed in gene expression data  and another is heterogeneous
characteristics often observed in a case group in case-control studies. The extension of the
logistic regression model  maxent  and AdaBoost for imbalanced data is discussed  providing a
new framework for improvement of prediction  classification  and performance of variable
selection. Weights functions introduced in the methods play an important role in alleviating
the imbalance of data. This book also furnishes a new perspective on these problem and shows
some applications of the recently developed statistical methods to real data sets.

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