Observation points classifier ensemble for high‐dimensional imbalanced classification

In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High-Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi-Dimensional Scaling (MDS) feature extraction technique. First, dimensionality of original imbalanced reduced MDS so that distances between any two different samples are preserved as well possible. Second, a novel OPCE applied classify by placing optimised observation points in low-dimensional space. Third, optimization point mappings carried out obtain reliable assessment unknown samples. Exhaustive experiments have been conducted evaluate feasibility, rationality, and effectiveness seven benchmark HDIC sets. Experimental results show (1) can be trained faster than high-dimensional data; (2) correctly identify number increased; (3) statistical analysis reveals yields better performances selected sets comparison eight other algorithms. This demonstrates viable problems.