Evolutionary Deep Fusion Method and its Application in Chemical Structure Recognition
提出一种进化算法来自动搜索最优的融合算子组合方案，用于融合多视角特征，并直接以图像输入进行化学结构识别，实验效果优于人工设计的方法。
Feature extraction is a critical issue in many machine learning systems. A number of basic fusion operators have been proposed and studied. This article proposes an evolutionary algorithm, called evolutionary deep fusion method, for searching an optimal combination scheme of different basic fusion operators to fuse multiview features. We apply our proposed method to chemical structure recognition. Our proposed method can directly take images as inputs, and users do not need to transform images to other formats. The experimental results demonstrate that our proposed method can achieve a better performance than those designed by human experts on this real-life problem.