id: 05622523
dt: a
an: 05622523
au: Gu, Qiong; Cai, Zhihua; Zhu, Li
ti: Classification of imbalanced data sets by using the hybrid re-sampling
    algorithm based on Isomap.
so: Cai, Zhihua (ed.) et al., Advances in computation and intelligence. 4th
    international symposium, ISICA 2009, Huangshi, China, October 23‒25,
    2009. Proceedings. Berlin: Springer (ISBN 978-3-642-04842-5/pbk).
    Lecture Notes in Computer Science 5821, 287-296 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: Isomap; Smote; NCR; re-sampling; Imbalanced data set
ci: 
li: doi:10.1007/978-3-642-04843-2_31
ab: Summary: The majority of machine learning algorithms previously designed
    usually assume that their training sets are well-balanced, but data in
    the real-world is usually imbalanced. The class imbalance problem is
    pervasive and ubiquitous, causing trouble to a large segment of the
    data mining community. As the conventional machine learning algorithms
    have bad performance when they learn from imbalanced data sets, it is
    necessary to find solutions to machine learning on imbalanced data
    sets. This paper presents a novel Isomap-based hybrid re-sampling
    approach to improve the conventional SMOTE algorithm by incorporating
    the Isometric feature mapping algorithm (Isomap). Experiment results
    demonstrate that this hybrid re-sampling algorithm attains a
    performance superior to that of the re-sampling. It is clear that the
    Isomap method is an effective means to reduce the dimension of the
    re-sampling. This provides a new possible solution for dealing with the
    IDS classification.
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