A framework for modeling positive class expansion with single snapshot

  • Yu Y
  • Zhou Z
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In many real-world data mining tasks, the coverage of the target concept
may change as the time changes. For example,the coverage of ��learned
knowledge�� of a student today may be different from his/er ��learned
knowledge�� tomorrow, since the ��learned knowledge�� of the student
is in expanding everyday. In order to learn a model capable of making
accurate predictions, the evolution of the concept must be considered,
and thus, a series of data sets collected at different time is needed.
However, in many cases there is only a single data set instead of
a series of data sets. In other words, only a single snapshot of
the data along the time axis is available. In this paper, we show
that for positive class expansion, i.e., the coverage of the target
concept is in expanding as illustrated in the above ��learned knowledge��
example, we can learn an accurate model from the single snapshot
data with the help of domain knowledge given by user. The effectiveness
of the proposed framework is validated in experiments.

Author-supplied keywords

  • Concept expansion
  • Data mining
  • Machine learning
  • PCES

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  • Yang Yu

  • Zhi Hua Zhou

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