Gene ontology annotation is known to be a very complicated multilabel classification task, and the hierarchical multilabel classification (HMC) approaches with local classifiers have been shown to be effective for the task. In a traditional HMC method, a set of hierarchically organized simple local classifiers are usually used, each of which for one hierarchical level separately. In this paper, we propose a novel hierarchical multilabel classifier implementing the whole set of hierarchically organized local classifiers in one deep convolution neural network (CNN) model with multiple heads and multiple ends (MHME). The proposed MHME CNN model consists of three parts: the body part of a deep CNN model shared by different local classifiers for feature extraction and feature mapping; the multiend part of a set of autoencoders performing feature fusion transforming the input vectors of different local classifiers to feature vectors with the same length so as to share the feature mapping part; and the multihead part of a set of linear multilabel classifiers. Furthermore, a sophisticated recursive algorithm is designed to train the MHME CNN model to realize the functions of a set of hierarchically organized local classifiers. In this way, by sharing a deep CNN with multiple local classifiers, we are able to construct more powerful local classifiers for each level with limited training samples, and to achieve better classification performance. Experiment results on various benchmark datasets show that the proposed deep CNN‐based model has better performance than the state‐of‐the‐art traditional models. Moreover, it gives rather good performance even under a transfer learning. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

中文翻译：

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使用多头和多端深度CNN模型的基因本体注释的分层多标签分类器

众所周知，基因本体标注是一项非常复杂的多标签分类任务，而具有局部分类器的分层多标签分类（HMC）方法已被证明对该任务有效。在传统的HMC方法中，通常使用一组分层组织的简单局部分类器，每个分类器分别用于一个分层级别。在本文中，我们提出了一种新颖的分层多标签分类器，该分类器在一个具有多个头和多个末端（MHME）的深度卷积神经网络（CNN）模型中实现了整个层次化组织的局部分类器。拟议的MHME CNN模型包括三个部分：由不同的本地分类器共享的深度CNN模型的主体部分，用于特征提取和特征映射；进行特征融合的一组自动编码器的多端部分，将不同局部分类器的输入向量转换为相同长度的特征向量，以共享特征映射部分；以及一组线性多标签分类器的多头部分。此外，设计了一种复杂的递归算法来训练MHME CNN模型，以实现一组分层组织的本地分类器的功能。这样，通过与多个本地分类器共享一个深层的CNN，我们可以使用有限的训练样本为每个级别构造更强大的本地分类器，并实现更好的分类性能。在各种基准数据集上的实验结果表明，所提出的基于CNN的深度模型比最新的传统模型具有更好的性能。此外，即使在转移学习的情况下，它也具有相当好的性能。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布