蔡毅,朱秀芳,孙章丽,陈阿娇.半监督集成学习综述[J].计算机科学,2017,44(Z6):7-13
半监督集成学习综述
Semi-supervised and Ensemble Learning:A Review
  
DOI:10.11896/j.issn.1002-137X.2017.6A.002
中文关键词:  半监督学习,集成学习,半监督集成学习,boosting,Bagging,泛化性能
英文关键词:Semi-supervised learning,Ensemble learning,Semi-supervised ensemble learning,Boosting,Bagging,Generalization performance
基金项目:本文受国家自然科学青年基金项目(41401479),高分辨率对地观测重大专项(民用部分)(02-Y30B06-9001-13115)资助
作者单位E-mail
蔡毅 北京师范大学地表过程与资源生态国家重点实验室 北京100875
北京师范大学资源学院 北京100875 
 
朱秀芳 北京师范大学地表过程与资源生态国家重点实验室 北京100875
北京师范大学资源学院 北京100875 
zhuxiufang@bnu.edu.cn 
孙章丽 北京师范大学地表过程与资源生态国家重点实验室 北京100875
北京师范大学资源学院 北京100875 
sunzhangli@gmail.com 
陈阿娇 湖南师范大学资源与环境科学学院 长沙410081 ajchen0807@163.com 
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中文摘要:
      半监督学习和集成学习是目前机器学习领域中两个非常重要的研究方向,半监督学习注重利用有标记样本与无标记样本来获得高性能分类器,而集成学习旨在利用多个学习器进行集成以提升弱学习器的精度。半监督集成学习是将半监督学习和集成学习进行组合来提升分类器泛化性能的机器学习新方法。首先,在分析半监督集成学习发展过程的基础上,发现半监督集成学习起源于基于分歧的半监督学习方法;然后,综合分析现有半监督集成学习方法,将其分为基于半监督的集成学习与基于集成的半监督学习两大类,并对主要的半监督集成方法进行了介绍;最后,对现有研究进了总结,并讨论了未来值得研究的问题。
英文摘要:
      Semi-supervised learning (SSL) and ensemble learning are two important paradigms in the field of machine learning research.SSL attempts to achieve strong generalization by exploiting both labeled and unlabeled instances,while ensemble learning aims to improve the performance of weak learner by making use of multiple classifiers.SSL ensemble learning is a novel paradigm which can improve the generalization performance of classifier by combining SSL and ensemble learning.Firstly the development process of SSL ensemble learning was analyzed and it was found that SSL ensemble learning is derived from disagreement-based SSL.Then,classify SSL Ensemble learning methods were classified into two categories:SSL-based ensemble learning and ensemble-based SSL.A detailed description for the main methods of SSL Ensemble learning was given.Finally,the current research status of SSL ensemble learning was summarized and some issues which are worth of further study were given.
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