Abstract:

As regards to the noise-sensitive, vulnerable to the missing values problem, the complexity and the large sparseness of real world data, and so on, propose to introduce ReliefF feature evaluation, that is to apply it into manifold learning. The experiments are divided into four cases: one is not to use any feature selection algorithm; one is to use only ReliefF feature evaluation; one is to use only the representative Locally Linear Embedding algorithm; and the last one is to use both. Results show that the classifying accuracy rate obtained by using the improved algorithm is higher than by ReliefF or Locally Linear Embedding respectively.

Key words:  feature selection, Relieff, manifold learning, classification