基于粒神经网络的多标签学习

doi:10.6040/j.issn.1671-9352.7.2023.239

摘要/Abstract

摘要： 引入粒计算理论,提出基于粒神经网络的多标签学习方法,采用相似度粒化的技术获得数据在结构上的相关性。样本在单特征上粒化成粒子,多特征上的粒子形成粒向量,并且定义粒子与粒向量的运算规则。在此基础上,引入粒损失函数,构建粒神经网络进行多标签学习,采用多个Mulan多标签数据集进行实验,在多种评价指标上与现有的多标签分类算法比较,结果表明了粒神经网络多标签学习算法的有效性与可行性。

关键词: 粒计算, 深度学习, 粒神经网络, 多标签学习, 粒向量

Abstract: This paper introduces the theory of granular computing and proposes a multi-label learning method based on granular neural networks. This method utilizes similarity granulation to capture the structural correlations in the data. Samples are granulated into granules on individual features, and granules across multiple features form granule vectors. Operations on granules and granule vectors are defined. On this basis, a granular loss function is introduced and a granular neural network is constructed for multi-label learning. Experiments are conducted on multiple Mulan multi-label datasets and compared with existing multi-label classification algorithms across various evaluation metrics. The results demonstrate the effectiveness and feasibility of the granular neural network multi-label learning algorithm.

Key words: granular computing, deep learning, granular neural networks, multi-label learning, granule vectors

中图分类号:

TP181

陈玉明,郑光宇,焦娜. 基于粒神经网络的多标签学习[J]. 《山东大学学报(理学版)》, 2024, 59(5): 1-11.

CHEN Yumin, ZHENG Guangyu, JIAO Na. Multi-label learning based on granular neural networks[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 1-11.

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