Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy

Abstract

The algorithm based on combination learning usually is superior to a single classification algorithm on the task of protein secondary structure prediction. However, the assignment of the weight of the base classifier usually lacks decision-making evidence. In this paper, we propose a protein secondary structure prediction method with dynamic self-adaptation combination strategy based on entropy, where the weights are assigned according to the entropy of posterior probabilities outputted by base classifiers. The higher entropy value means a lower weight for the base classifier. The final structure prediction is decided by the weighted combination of posterior probabilities. Extensive experiments on CB513 dataset demonstrates that the proposed method outperforms the existing methods, which can effectively improve the prediction performance.