DG-subspace: A novel attributes selection method for lazy learning

Lazy learning has shown promising reliability in data stream classification mining, which suffer from `Curse of dimensionality' in broad applications. Conventional Attribute selection methods always seek promising subspace by ranking all the attributes, which is not suitable for lazy learning, and suffer from high computing complexity. We proposed a novel attributes selection method `DistinGuishing Subspace (DG-Subspace)', which lay high values on the performance of attributes as a group instead of single attribute with higher ranks. `DistinGuishing Pattern Tree (DGP-tree)' was formed to compress dataset, based on which a heuristic method to seek DG-subspace was raised, with linear scalability. Theoretic analysis and numeric experiment justified the effectiveness and efficiency of the method.