摘要
传统的单标签数据挖掘技术研究对象中,每个样本仅属于一个类别标签,但在实际应用中一个样本更倾向于同时具备多个属性,即属于多标签数据类型。多标签数据挖掘技术现已成为数据挖掘技术中的一个研究热点。其研究成果广泛地应用于各种不同的领域,如图像视频的语义标注、功能基因组、音乐情感分类以及营销指导等。从多标签数据挖掘的方法和度量方式两个方面对多标签数据挖掘进行了系统详细的阐述,最后归纳了目前研究中存在的问题和挑战并展望了本领域的发展趋势。
In traditional single-label data mining,each sample belongs to only one label.However,an real-world object usually associates with more than one attribute.Multi-label data mining is motivated by increasing requirements of mo-dern applications and is widely applied in many fields,such as semantic annotation of images and video,functional geno-mics,music categorization into emotions and directed marketing,which has attracted significant attention from a lot of researchers.This paper systematically introduced technology of multi-label data mining from two aspects：methods and evaluation metrics.Finally,we summarized some problems and challenges in current study and gave prospects of the tendency in this area.
出处
《计算机科学》
CSCD
北大核心
2013年第4期14-21,共8页
Computer Science
基金
国家自然科学基金(60970070
61033007
60803043)资助
关键词
多标签
数据挖掘
分类
排序
度量
Multi-label
Data mining
Classification
Ranking
Metrics
