On Classification from Outlier View
  Classification is the basis of cognition. Unlike other solutions, this study
approaches it from the view of outliers. We present an expanding algorithm to
detect outliers in univariate datasets, together with the underlying
foundation. The expanding algorithm runs in a holistic way, making it a rather
robust solution. Synthetic and real data experiments show its power.
Furthermore, an application for multi-class problems leads to the introduction
of the oscillator algorithm. The corresponding result implies the potential
wide use of the expanding algorithm.
