机器学习分类问题及算法研究综述
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摘要
分类问题及其算法是机器学习的一个重要分支,其应用越来越广泛,相关算法及应用研究取得了长足进展。文章对近年来机器学习分类算法的研究成果进行了回顾,从单一分类算法到集成分类算法分别进行总结,比较了不同分类算法的核心思想、优缺点以及实际应用,并分析了机器学习分类算法研究所面临的挑战和发展趋势。
Classification and its algorithm are an important branch of machine learning, whose application is more and more extensive, and related algorithms and application research have made great progress. This paper firstly reviews the research results of machine-learning classification algorithm in recent years, and then makes a respective summary on single classification algorithm and integrated classification algorithm. The paper also makes a comparison of the core ideas, advantages and disadvantages and practical applications of different classification algorithms. Finally the paper analyzes the challenges that the research on machine-learning classification algorithm is facing and its future developing trend.
Classification and its algorithm are an important branch of machine learning, whose application is more and more extensive, and related algorithms and application research have made great progress. This paper firstly reviews the research results of machine-learning classification algorithm in recent years, and then makes a respective summary on single classification algorithm and integrated classification algorithm. The paper also makes a comparison of the core ideas, advantages and disadvantages and practical applications of different classification algorithms. Finally the paper analyzes the challenges that the research on machine-learning classification algorithm is facing and its future developing trend.
引文
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[28]Freund Y, Schapire R E. A Decision-theoretic Generalization of On-line Learning and an Application to Boosting[J]. Journal of Com?puter and System Sciences,1997,55(1).
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[32]秦姣龙,王蔚. Bagging组合的不平衡数据分类方法[J].计算机工程,2011,37(14).
[2]Etherm A.机器学习导论[M].范明,昝红英,牛常勇译.北京:机械工业出版社,2009.
[3]Zaslavsky G M. A Survey of Classifica?tion Methods in Data Streams[M]. Berlin:Springer, 2009.
[4]赵春霞,钱乐祥.遥感影像监督分类与非监督分类的比较[J].河南大学学报(自然科学版), 2004,(3).
[5]Armitage W. A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification[J].Computer Communication Review, 2006, 30(1).
[6]李玲俐.数据挖掘中分类算法综述[J].重庆师范大学学报(自然科学版), 2011, 28(4).
[7]张润,王永滨.机器学习及其算法和发展研究[J].中国传媒大学学报(自然科学版), 2016, 23(2).
[8]Maron M E, Kuhns J L. On Relevance Probabilistic Indexing and In?formation Retrieval[J]. Journal of the ACM(JACM), 1960, 7(3).
[9]Cover T M, Hart P E.Nearest Neighbor Pattern Classification[J].IEEE Transactions on Information Theory, 1967, 13(1).
[10]Breiman L, Friedman J, Olshen R A,et al.Classification and regres?sion trees[M].Belmont:Wadsworth,1984.
[11]Quinlan J R.C4.5:Programs for Machine Learning[M].Morgan Kauff?man, 1993.
[12]Thombre, A. Comparing Logistic Regression, Neural Networks, C5.0and m5'Classification Techniques[J]. Lecture Notes in Computer Science, 2012, 7376.
[13]Xie N, Liu Y. Review of Decision Trees[J].Computer Science and In?formation Technology(ICCSIT),2010,(5).
[14]丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1).
[15]朱虎明,李佩焦,李成等.深度神经网络并行化研究综述[J].计算机学报,2018, 41(8).
[16]王锋,王艳娜,梁义涛等.基于KNN算法的小麦隐蔽性虫害分类器设计[J].农机化研究,2014,36(7).
[17]吕利利,颉耀文,黄晓君等.基于CART决策树分类的沙漠化信息提取方法研究[J].遥感技术与应用,2017, 32(3).
[18]徐曌,张斌.基于约简矩阵和C4.5决策树的故障诊断方法[J].计算机技术与发展,2018,(2).
[19]王忠民,张琮,衡霞. CNN与决策树结合的新型人体行为识别方法研究[J].计算机应用研究,2017, 34(12).
[20]Hansen L K, Salamon P. Neural Network Ensembles[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1990, 12(10).
[21]张春霞,张讲社.选择性集成学习算法综述[J].计算机学报,2011,34(8).
[22]Breiman L. Bagging Predicators[J]. Machine Learning,1996, 24(2).
[23]Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1).
[24]Ishwaran H, Kogalur U B, Blackstone E H, et al. Random Survival Forests[J]. The Annals of Applied Statistics,2008, 2(3).
[25]Desir C, Petitjean C, Heutte, L, et al. Classification of Endomicro?scopic Images of the Lung Based on Random Subwindows and Ex?tra-Trees[J]. IEEE Transactions on Biomedical Engineering,2012,59(9).
[26]Liu F, Ting K, Zhou Z. Isolation Forest[C]. Proceeding of the 8th IEEE International Conference on Data Mining,2008.
[27]沈学华,周志华,吴建鑫等. Boosting和Bagging综述[J].计算机工程与应用, 2000,(12).
[28]Freund Y, Schapire R E. A Decision-theoretic Generalization of On-line Learning and an Application to Boosting[J]. Journal of Com?puter and System Sciences,1997,55(1).
[29]于玲,吴铁军.集成学习:Boosting算法综述[J].模式识别与人工智能,2004,17(1).
[30]Friedman J, Hastie T.Additive Logistic Regression:A Statistical View of Boosting(With Discussions)[J].Annals of Statistics,2000,(28).
[31]Ma X, Ding C, Luan S, et al. Prioritizing Influential Factors for Free?way Incident Clearance Time Prediction Using the Gradient Boost?ing Decision Trees Method[J].IEEE Trans on Intelligent Transporta?tion Systems, 2017,99.
[32]秦姣龙,王蔚. Bagging组合的不平衡数据分类方法[J].计算机工程,2011,37(14).
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