Construction of Graduate Student Quality Policy System Based on NMIFS Algorithm

Abstract

In order to improve the graduate matriculate quality in the Internet era, this paper analyzes the policy system for ensuring the graduate matriculate quality in the Internet era, and improves the policy system for postgraduate student quality by combining intelligent data processing methods. This paper proposes a strategy based on NMIFS dimensionality reduction and threshold optimization. Moreover, this paper analyzes the impact of large-scale cubes on the construction of isolated trees, and proposes a dimensionality reduction strategy based on the NMIFS algorithm. In addition, this paper conducts a dimensionality reduction strategy comparison experiment on the four algorithms in projection transformation and feature selection, which verifies the effectiveness of the NMIFS feature selection dimensionality reduction strategy for the construction of isolated forests. The experimental study verifies that the policy system of graduate matriculate quality proposed in this paper can effectively improve the graduate matriculate quality.