Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is a recently proposed algorithm for ensembles of k-nn classifiers . FASBIR works by first performing a global filtering of attributes using information gain, then randomising the bagged ensemble with random subsets of the remaining attributes and random distance metrics. In this paper we propose two refinements of FASBIR and evaluate them on several very large data sets.
|Number of pages||8|
|Publication status||Published - 2006|
|Event||Feature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining - Bethesda, United States|
Duration: 22 Apr 2006 → …
|Conference||Feature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining|
|Period||22/04/06 → …|