Fast Learning and Testing for Imbalanced Multi-Class Changes in Streaming Data by Dynamic Multi-Stratum Network

Abstract

Although several efficient learning methods have recently been proposed to handle class drift situations, issues remain in various streaming data applications that possibly deteriorate classification accuracy. Three important issues were considered, that is: 1) lifetime and class changes; 2) high imbalance ratios of streaming data among classes; and 3) classification accuracy of untrained data and class-changed data. A new dynamical learning structure based on hyper-elliptical capsule and multi-stratum network was introduced to cope with these issues. The experimental results on a simulated University of California at Irvine non-concept-drift database and real concept-drift data confirm that the proposed multi-stratum learning provided better accuracy, faster learning speed, and lower structural complexity than other concept-drift algorithms.