《计算机应用研究》|Application Research of Computers

最优路径森林分类算法综述

Review on optimum-path forest classification algorithm

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作者 沈龙凤,宋万干,葛方振,李想,杨忆,刘怀愚,高向军,洪留荣
机构 淮北师范大学 计算机科学与技术学院,安徽 淮北 235000
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文章编号 1001-3695(2018)01-0007-06
DOI 10.3969/j.issn.1001-3695.2018.01.002
摘要 针对快速分类算法中最优路径森林(OPF)分类算法进行了研究,进行了OPF分类算法研究及应用现状的调查。OPF算法是近期兴起的一种基于完全图的分类算法,在一些公共数据集上与支持向量机(SVM)、人工神经网络(ANN)等算法的对比中,该算法能取得类似或更好的结果,速度更快。该算法不依赖于任何参数、不需要参数优化、不需要对各类别的形状作任何假设,能够处理多类问题,旨在全面系统地介绍OPF算法的研究及应用进展。
关键词 最优路径森林;分类;完全图
基金项目 安徽省高等学校自然科学基金一般项目(KJ2016B018)
安徽省高等学校自然科学研究重大项目(KJ2017ZD32)
安徽省高校管理大数据研究中心2017年招标课题项目经费资助项目(25500119)
淮北师范大学2017年校级质量工程项目(12801262,12801240)
安徽省高校管理大数据研究中心2016年招标课题项目经费资助项目(12500347)
本文URL http://www.arocmag.com/article/01-2018-01-002.html
英文标题 Review on optimum-path forest classification algorithm
作者英文名 Shen Longfeng, Song Wangan, Ge Fangzhen, Li Xiang, Yang Yi, Liu Huaiyu, Gao Xiangjun, Hong Liurong
机构英文名 SchoolofComputerScience&Technology,HuaibeiNormalUniversity,HuaibeiAnhui235000,China
英文摘要 This paper did the research on optimal-path forest (OPF) classification algorithm for fast classification algorithm. It investigated the research and application of the OPF classification algorithm. The OPF algorithm is a new classification algorithm based on complete graph. In some public data sets, OPF was compared with support vector machine(SVM) and artificial neural network(ANN), the OPF algorithm could achieve similar or better results, but faster than them. The OPF algorithm does not depend on any parameters, does not need parameter optimization, and also can solve any problems without making any assumptions about the shape of each class. This paper aims to introduce the research status and future research directions of the OPF algorithm to the domestic readers.
英文关键词 optimum-path forest; classification; complete graph
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  [1] Jin Wen, Li Zhaojia, Wei Luosi, et al. The improvements of BP neural network learning algorithm[C] //Proc of the 5th International Conference on Signal Processing Proceedings. [S. l. ] :IEEE Press, 2000:1647-1649.
[2] Zhou Zhihua, Wu Jianxin, Tang Wei. Ensembling neural networks:many could be better than all[J] . Artificial Intelligence, 2002, 137(1-2):239-263.
[3] Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers[C] //Proc of the 5th Annual Workshop on Computational Learning Theory. New York:ACM Press, 1992:144-152.
[4] Platt J C. Fast training of SVMs using sequential minimal optimization[C] //Advances in Kernel Methods. Cambridge:MIT Press, 1999:185-208.
[5] Keerthi S S, Shevade S K, Bhattacharyya C, et al. Improvements to Platt’s SMO algorithm for SVM classifier design[J] . Neural Computation, 2001, 13(3):637-649.
[6] Dong Jianxiong, Krzyzak A, Suen C Y. A fast SVM training algorithm[J] . International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(3):367-384.
[7] Hunt E B, Marin J, Stone P T. Experiments in induction[M] . New York:Academic Press, 1966.
[8] Quinlan J R. Induction of decision trees[J] . Machine Learning, 1986, 1(1):81-106.
[9] Quinlan J R. C4. 5:programming for machine learning[M] . San Francisco:Morgan Kauffmann Publisher, 1993:27-48.
[10] Mehta M, Agrawal R, Rissanen J. SLIQ:a fast scalable classifier for data mining[C] //Proc of International Conference on Extending Database Technology. Berlin:Springer, 1996:18-32.
[11] Olaru C, Wehenkel L. A complete fuzzy decision tree technique[J] . Fuzzy Sets and Systems, 2003, 138(2):221-254.
[12] 冯少荣. 决策树算法的研究与改进[J] . 厦门大学学报:自然科学版, 2007, 46(4):496-500.
[13] Kwok S W, Carter C. Multiple decision trees[C] //Proc of the 4th Annual Conference on Uncertainty in Artificial Intelligence. [S. l. ] :North-Holland Publishing Co, 1990:327-338.
[14] Murphy K P. Naive Bayes classifiers[D] . Vancouver:University of British Columbia, 2006.
[15] Duda R O, Hart P E. Pattern classification and scene analysis[M] . New York:Wiley, 1973.
[16] Langley P, Iba W, Thompson K. An analysis of Bayesian classifiers[C] //Proc of the 10th National Conference on Artificial Intelligence. 1992:223-228.
[17] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers[J] . Machine Learning, 1997, 29(2-3):131-163.
[18] Cheng Jie, Greiner R. Comparing Bayesian network classifiers[C] //Proc of the 15th Conference on Uncertainty in Artificial Intelligence. San Francisco:Morgan Kaufmann Publishers Inc, 1999:101-108.
[19] Papa J P, Falco A X, Suzuki C T N, et al. A discrete approach for supervised pattern recognition[C] //Proc of International Workshop on Combinatorial Image Analysis. Berlin:Springer, 2008:136-147.
[20] Papa J P, Falco A X, Miranda P A V, et al. Design of robust pattern classifiers based on optimum-path forests[C] //Proc of International Symposium on Mathematical Morphology and Its Applications to Image and Signal Proces-sing. 2007:337-348.
[21] Papa J P, Falco A X. A learning algorithm for the optimum-path fo-rest classifier[C] //Proc of International Workshop on Graph-Based Representations in Pattern Recognition. Berlin:Springer, 2009:195-204.
[22] Papa J O P, Falc A X, De Albuquerque V H C, et al. Efficient supervised optimum-path forest classification for large datasets[J] . Pattern Recognition, 2012, 45(1):512-520.
[23] Papa J P, Falco A X. A new variant of the optimum-path forest classifier[C] //Proc of International Symposium on Visual Computing. Berlin:Springer, 2008:935-944.
[24] Chiachia G, Marana A N, Papa J P, et al. Infrared face recognition by optimum-path forest[C] //Proc of the 16th International Confe-rence on Systems, Signals and Image Processing. [S. l. ] :IEEE Press, 2009:1-4.
[25] Papa J P, Falcao A X, Levada A L M, et al. Fast and accurate holistic face recognition using optimum-path forest[C] //Proc of the 16th International Conference on Digital Signal Processing. [S. l. ] :IEEE Press, 2009:1-6.
[26] Lopes R, Costa K, Papa J. On the evaluation of tensor-based representations for optimum-path forest classification[C] //Proc of IAPR Workshop on Artificial Neural Networks in Pattern Recognition. [S. l. ] :Springer International Publishing, 2016:117-125.
[27] Montoya-Zegarra J A, Papa J P, Leite N J, et al. Novel approaches for exclusive and continuous fingerprint classification[C] //Proc of Pacific-Rim Symposium on Image and Video Technology. Berlin:Springer, 2009:386-397.
[28] Iliev A I, Scordilis M S, Papa J P, et al. Spoken emotion recognition through optimum-path forest classification using glottal features[J] . Computer Speech & Language, 2010, 24(3):445-460.
[29] Papa J P, Marana A N, Spadotto A A, et al. Robust and fast vowel recognition using optimum-path forest[C] //Proc of IEEE Internatio-nal Conference on Acoustics, Speech and Signal Processing. [S. l. ] :IEEE Press, 2010:2190-2193.
[30] Lopes G S, De Silva D C, Rodrigues A W O, et al. Recognition of handwritten digits using the signature features and optimum-path forest classifier[J] . IEEE Latin America Transactions, 2016, 14(5):2455-2460.
[31] Nakamura R, Pereira L, Silva D, et al. Fast robot voice interface through optimum-path forest[C] //Proc of the 16th International Conference on Intelligent Engineering Systems. [S. l. ] :IEEE Press, 2012:67-71.
[32] Pisani R J, Papa J P, Zimback C R L, et al. Land use classification using optimum-path forest[C] //Proc of the 14th Brazilian Symposium on Remote Sensing. 2009:7063-7070.
[33] Pisani R, Riedel P, Ferreira M, et al. Land use image classification through optimum-path forest clustering[C] //Proc of IEEE International Geoscience and Remote Sensing Symposium. 2011:826-829.
[34] Osaku D, Pereira D R, Levada A L M, et al. Fine-tuning contextual-based optimum-path forest for land-cover classification[J] . IEEE Geoscience and Remote Sensing Letters, 2016, 13(5):735-739.
[35] Osaku D, Levada A L M, Papa J P. A block-based Markov random field model estimation for contextual classification using optimum-path forest[C] //Proc of IEEE International Symposium on Circuits and Systems. 2016:994-997.
[36] Guilherme I R, Marana A N, Papa J P, et al. Fast petroleum well drilling monitoring through optimum-path forest[J] . Journal of Next Generation Information Technology, 2010, 1(1):77-85.
[37] Marques C M, Guilherme I R, Nakamura R Y M, et al. New trends in musical genre classification using optimum-path forest[C] //Proc of the 12th International Society for Music Information Retrieval Confe-rence. 2011:699-704.
[38] Papa J P, Spadotto A A, Falcao A X, et al. Optimum path forest classifier applied to laryngeal pathology detection[C] //Proc of the 15th International Conference on Systems, Signals and Image Proces-sing. [S. l. ] :IEEE Press, 2008:249-252.
[39] Cappabianco F A M, Falcao A X, Rocha L M. Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain[C] //Proc of the 5th IEEE International Symposium on Biomedical Imaging:From Nano to Macro. 2008:428-431.
[40] Cappabianco F A M, Falco A X, Yasuda C L, et al. Brain tissue MR-image segmentation via optimum-path forest clustering[J] . Computer Vision and Image Understanding, 2012, 116(10):1047-1059.
[41] Spadoto A A, Guido R C, Papa J P, et al. Parkinson’s disease identification through optimum-path forest[C] //Proc of Annual International Conference of the IEEE Engineering in Medicine and Biology. 2010:6087-6090.
[42] Spadoto A A, Guido R C, Carnevali F L, et al. Improving Parkinson’s disease identification through evolutionary-based feature selection[C] //Proc of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011:7857-7860.
[43] Cappabianco F, Ide J S, Falco A, et al. Automatic subcortical tissue segmentation of MR images using optimum-path forest clustering[C] //Proc of the 18th IEEE International Conference on Image Processing. 2011:2653-2656.
[44] Albuquerque V H C, Papa J P, Falco A X, et al. Application of optimum-path forest classifier for synthetic material porosity segmentation[C] //Proc of the 17th International Conference on Systems, Signals and Image Processing. 2010:1-4.
[45] Papa J P, De Albuquerque V H C, Falco A X, et al. Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest[C] //Proc of International Symposium Computational Mo-deling of Objects Represented in Images. Berlin:Springer, 2010:210-220.
[46] Freitas G M, Avila A M H, Papa J P, et al. Optimum-path forest-based rainfall estimation[C] //Proc of the 16th International Confe-rence on Systems, Signals and Image Processing. 2009:1-4.
[47] Papa J P, Falcao A X, De Freitas G M, et al. Robust pruning of training patterns for optimum-path forest classification applied to satellite-based rainfall occurrence estimation[J] . IEEE Geoscience and Remote Sensing Letters, 2010, 7(2):396-400.
[48] Papa J P, Gutierrez M E M, Nakamura R Y M, et al. Automatic classification of fish germ cells through optimum-path forest[C] //Proc of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011:5084-5087.
[49] Pereira C, Nakamura R, Papa J P, et al. Intrusion detection system using optimum-path forest[C] //Proc of the 36th Conference on Local Computer Networks. 2011:183-186.
[50] Pereira C R, Nakamura R Y M, Costa K A P, et al. An optimum-path forest framework for intrusion detection in computer networks[J] . Engineering Applications of Artificial Intelligence, 2012, 25(6):1226-1234.
[51] Costa K, Pereira C, Nakamura R, et al. Boosting optimum-path fo-rest clustering through harmony search and its applications for intrusion detection in computer networks[C] //Proc of the 4th Internatio-nal Conference on Computational Aspects of Social Networks. 2012:181-185.
[52] Costa K A P, Pereira L A M, Nakamura R Y M, et al. A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks[J] . Information Sciences, 2015, 294(C):95-108.
[53] Ramos C C O, Souza A N, Papa J P, et al. Fast non-technical losses identification through optimum-path forest[C] //Proc of the 15th International Conference on Intelligent System Applications to Power Systems. 2009:1-5.
[54] Ramos C C O, De Sousa A N, Papa J P, et al. A new approach for nontechnical losses detection based on optimum-path forest[J] . IEEE Trans on Power Systems, 2011, 26(1):181-189.
[55] Ramos C C O, Souza A N, Nakamura R Y M, et al. Electrical consumers data clustering through optimum-path forest[C] //Proc of the 16th International Conference on Intelligent System Application to Power Systems. 2011:1-4.
[56] Júnior L A P, Ramos C C O, Rodrigues D, et al. Unsupervised non-technical losses identification through optimum-path forest[J] . Electric Power Systems Research, 2016, 140:413-423.
[57] Di Martino M, Decia F, Molinelli J, et al. A novel framework for nontechnical losses detection in electricity companies[M] //Pattern Recognition-Applications and Methods. Berlin:Springer, 2013:109-120.
收稿日期 2016/12/22
修回日期 2017/3/1
页码 7-12,23
中图分类号 TP301.6
文献标志码 A