Dimensionality Reduction by Feature Co-Occurrence based Rough Set
Lei La · International Journal of Performability Engineering · 2019
Feature selection is the key issue of unstructured data mining related fields.This paper presents a dimensionality reduction method which uses a rough set as the feature selection tool.Different from previous rough set based classification algorithm, it takes feature cooccurrence into account when make attribution reduction to get a more accurate feature subset.The novel method called Feature Cooccurrence Quick Reduction algorithm is in this article.Experimental results show it has a high efficiency in dimensionality reductiontime consumption by approximately 23% less than traditional rough set based dimensionality reduction methods.Moreover, classification based on the feature set selected by Feature Co-occurrence Quick Reduction algorithm is more precise.The proposed algorithm is helpful to us for refining knowledge from massive unstructured data.