A Novel Approach for Classify Different Classes in Large Dimensional problems based on Feature Extraction and Selection
Fernando Emami · 2012
Feature extraction and have choice square measure 2 general ways for the side of spatial property that it is still a huge drawback in the pattern recognition context. In this paper, a novel approach has been planned to classify totally different categories in giant dimensional issues. A 2 layer feature reduction is planned here. First, a parallel cooperative feature choice is applied to knowledge and second, knowledge is remodeled in a very new feature house. between large selection of usable analysis to choose best set of options in a very dataset, Tabu Search (TS) is one of acceptable and progressive ways. when ample iterations that satisfy the objective perform the most effective set has been calculated by option between 2 reduction phases and then knowledge is sent into these set to a brand new house once we attempt to take away hissing options by a feature extraction approach. Direct linear discrimination associate degreealysis (D-LDA) has been used as an economical feature extraction ways. Finally, knowledge are classified by Support Vector Machine (SVM) as a prevailing used classifier. Filters and Wrappers square measure the 2 ancient sorts of objective functions for feature choice. each of those satisfactory measures have been enforced and their results on the customary UCI dataset have been shown. The results show the superiority of our combinatorial approach in comparison with the ancient ways.