A classification learning algorithm of SBSCLearning
using stepbystep learning model
1. School of Management, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract：The baseclassifiers of the Learn++.NSE are separate, and the previous classifiers cannot help the forming of subsequent classifiers. The classification accuracy rate of the algorithm should be improved further. To solve the problem, drawing on the experience of the human learning process, the learning mechanism within the Learn++.NSE algorithm was optimized by the proposed SBSCLearning gradual learning algorithm to transform the original independent learning of baseclassifiers into a stepbystep learning. The disadvantages of Learn++.NSE were analyzed, and the process of SBSCLearning algorithm was given. The incremental learning was conducted first on the basis of baseclassifier, and the final ensemble result was then finished. The classification accuracy rates of SBSCLearning and Learn++.NSE were compared based on the test data. The experimental results show that the SBSCLearning has the advantages of both incremental learning and ensemble learning and can improve the classification accuracy compared with the Learn++.NSE. For the artificial SEA data, the average classification accuracy rates of SBSCLearning and Learn++.NSE are 0.982 and 0.976, respectively. For the real rotating checkerboard data, under different Constant, Sinusoidal and Pulse environments, the average classification accuracy rates of SBSCLearning are 0.624, 0.655 and 0.662 with those of Learn++.NSE of 0.593, 0.633 and 0.629, respectively.
2018, Vol. 39 
