基于粒神经网络的多标签学习
Multi-label learning based on granular neural networks
陈玉明 1郑光宇 1焦娜2
作者信息
- 1. 厦门理工学院计算机与信息工程学院,福建 厦门 361024
- 2. 华东政法大学刑事法学院,上海 201620
- 折叠
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.
Abstract
This paper introduces the theory of granular computing and proposes a multi-label learning method based on granular neu-ral 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 vec-tors 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 al-gorithms across various evaluation metrics.The results demonstrate the effectiveness and feasibility of the granular neural network multi-label learning algorithm.
关键词
粒计算/深度学习/粒神经网络/多标签学习/粒向量Key words
granular computing/deep learning/granular neural networks/multi-label learning/granule vectors引用本文复制引用
出版年
2024
NETL
NSTL
万方数据