Improving Neural Network Classifier using Gradient-based Floating Centroid Method
This work addresses a computational bottleneck for neural network classifiers, though it appears incremental as it adapts an existing method to a more efficient optimization approach.
The paper tackled the inefficiency of evolutionary computation in the floating centroid method for neural network classifiers by introducing a gradient-based floating centroid (GDFC) method with a new loss function, resulting in promising classification performance on benchmark datasets.
Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.