Evolutionary Multi-View Classification via Eliminating Individual Fitness Bias
Xinyan Liang, Shuai Li, Qian Guo, Yuhua Qian, Bingbing Jiang, Tingjin Luo, Liang Du
Multi-view classification (MVC) methods based on evolutionary algorithms have been widely recognized in MVC community due to their adaptive mechanisms. Fitness evaluation (FE) that aims to compute the classification performance of each individual from population, is one core step of such methods. Its accuracy directly determines the correctness of the evolutionary direction. However, there are significant differences in the information content among views involved by an individual. The adoption of a joint training strategy will limit the mining of key information in views with low information content in this process. This leads to suboptimal utilization of multi-view data, causing the model not to achieve optimal performance when converging, and thus triggering the fitness evaluation bias (FEB) problem. In view of the crucial role of FE, once FEB occurs, it will lead to the distortion of the entire evolutionary process, and the deviation will gradually accumulate with each iteration. Eventually, it will mislead the evolutionary direction and cause the model to fall into a local optimum. Regrettably, the existing evolutionary multi-view classification methods have not paid attention to this issue. To address the issue, we propose an evolutionary multi-view classification via eliminating individual fitness bias approach (EFB-EMVC) where the FEB problem is addressed by introducing evolutionary navigators for each multi-view model, thereby calibrating the evolutionary direction in real time. Experimental results on nine multi-view datasets demonstrate that this simple yet powerful strategy is effective in addressing the FEB problem and the method armed with this strategy exhibits superior performance compared to other evolutionary multi-view classification methods.  (The code will be published.)