메뉴 건너뛰기




Volumn 45, Issue 3, 2015, Pages 416-429

Boosting for multi-graph classification

Author keywords

Boosting; graph classification; multi graph; multi instance learning; subgraph mining

Indexed keywords

ITERATIVE METHODS; WEBSITES;

EID: 85027920186     PISSN: 21682267     EISSN: 21682275     Source Type: Journal    
DOI: 10.1109/TCYB.2014.2327111     Document Type: Article
Times cited : (109)

References (57)
  • 1
    • 24344484786 scopus 로고    scopus 로고
    • Frequent substructure-based approaches for classifying chemical compounds
    • Aug.
    • M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis, "Frequent substructure-based approaches for classifying chemical compounds," IEEE Trans. Knowl. Data Eng., vol. 17, no. 8, pp. 1036-1050, Aug. 2005.
    • (2005) IEEE Trans. Knowl. Data Eng. , vol.17 , Issue.8 , pp. 1036-1050
    • Deshpande, M.1    Kuramochi, M.2    Wale, N.3    Karypis, G.4
  • 2
    • 0742268827 scopus 로고    scopus 로고
    • An efficient and scalable algorithm for clustering XML documents by structure
    • Jan.
    • W. Lian, D.-L. Cheung, N. Mamoulis, and S.-M. Yiu, "An efficient and scalable algorithm for clustering XML documents by structure," IEEE Trans. Knowl. Data Eng., vol. 16, no. 1, pp. 82-96, Jan. 2004.
    • (2004) IEEE Trans. Knowl. Data Eng. , vol.16 , Issue.1 , pp. 82-96
    • Lian, W.1    Cheung, D.-L.2    Mamoulis, N.3    Yiu, S.-M.4
  • 3
    • 77956304974 scopus 로고    scopus 로고
    • Mining graph patterns efficiently via randomized summaries
    • Lyon, France
    • C. Chen et al., "Mining graph patterns efficiently via randomized summaries," in Proc. 35th Int. Conf. VLDB, Lyon, France, 2009, pp. 742-753.
    • (2009) Proc. 35th Int. Conf. VLDB , pp. 742-753
    • Chen, C.1
  • 4
    • 78149341724 scopus 로고    scopus 로고
    • Image categorization using directed graphs
    • Crete, Greece
    • H. Wang, H. Huang, and C. Ding, "Image categorization using directed graphs," in Proc. 11th ECCV, Crete, Greece, 2010, pp. 762-775.
    • (2010) Proc. 11th ECCV , pp. 762-775
    • Wang, H.1    Huang, H.2    Ding, C.3
  • 5
    • 33750309516 scopus 로고    scopus 로고
    • Graph-based text classification: Learn from your neighbors
    • Seattle, WA, USA
    • R. Angelova and G. Weikum, "Graph-based text classification: Learn from your neighbors," in Proc. 29th Annu. Int. ACM SIGIR, Seattle, WA, USA, 2006, pp. 485-492.
    • (2006) Proc. 29th Annu. Int. ACM SIGIR , pp. 485-492
    • Angelova, R.1    Weikum, G.2
  • 6
    • 34948865790 scopus 로고    scopus 로고
    • Image classification with segmentation graph kernels
    • Minneapolis, MN, USA
    • Z. Harchaoui and F. Bach, "Image classification with segmentation graph kernels," in Proc. 20th IEEE Conf. CVPR, Minneapolis, MN, USA, 2007, pp. 1-8.
    • (2007) Proc. 20th IEEE Conf. CVPR , pp. 1-8
    • Harchaoui, Z.1    Bach, F.2
  • 7
    • 78149312583 scopus 로고    scopus 로고
    • Frequent subgraph discovery
    • M. Kuramochi and G. Karypis, "Frequent subgraph discovery," in Proc. 1st ICDM, 2001, pp. 313-320.
    • (2001) Proc. 1st ICDM , pp. 313-320
    • Kuramochi, M.1    Karypis, G.2
  • 8
    • 77956220358 scopus 로고    scopus 로고
    • Near-optimal supervised feature selection among frequent subgraphs
    • M. Thoma et al., "Near-optimal supervised feature selection among frequent subgraphs," in Proc. 9th SDM, 2009, pp. 1075-1086.
    • (2009) Proc. 9th SDM , pp. 1075-1086
    • Thoma, M.1
  • 9
    • 84881358578 scopus 로고    scopus 로고
    • Graph stream classification using labeled and unlabeled graphs
    • Brisbane, QLD, USA
    • S. Pan, X. Zhu, C. Zhang, and P. Yu, "Graph stream classification using labeled and unlabeled graphs," in Proc. 29th IEEE ICDE, Brisbane, QLD, USA, 2013, pp. 398-409.
    • (2013) Proc. 29th IEEE ICDE , pp. 398-409
    • Pan, S.1    Zhu, X.2    Zhang, C.3    Yu, P.4
  • 10
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artif. Intell., vol. 97, nos. 1-2, pp. 273-324, 1997.
    • (1997) Artif. Intell. , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 11
    • 0030649484 scopus 로고    scopus 로고
    • Solving the multiple instance problem with axis-parallel rectangles
    • T. Dietterich, R. Lathrop, and T. Lozano-Pérez, "Solving the multiple instance problem with axis-parallel rectangles," Artif. Intell., vol. 89, no. 1-2, pp. 31-71, 1997.
    • (1997) Artif. Intell. , vol.89 , Issue.1-2 , pp. 31-71
    • Dietterich, T.1    Lathrop, R.2    Lozano-Pérez, T.3
  • 12
    • 79953031810 scopus 로고    scopus 로고
    • MILIS: Multiple instance learning with instance selection
    • May
    • Z. Fu, A. Robles-Kelly, and J. Zhou, "MILIS: Multiple instance learning with instance selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 958-977, May 2011.
    • (2011) IEEE Trans. Pattern Anal. Mach. Intell. , vol.33 , Issue.5 , pp. 958-977
    • Fu, Z.1    Robles-Kelly, A.2    Zhou, J.3
  • 13
    • 15544389390 scopus 로고    scopus 로고
    • Multi-instance learning based web mining
    • Z.-H. Zhou, K. Jiang, and M. Li, "Multi-instance learning based web mining," Appl. Intell., vol. 22, no. 2, pp. 135-147, 2005.
    • (2005) Appl. Intell. , vol.22 , Issue.2 , pp. 135-147
    • Zhou, Z.-H.1    Jiang, K.2    Li, M.3
  • 14
    • 79952898899 scopus 로고    scopus 로고
    • Weakly supervised training of a sign language recognition system using multiple instance learning density matrices
    • Apr.
    • D. Kelly, J. McDonald, and C. Markham, "Weakly supervised training of a sign language recognition system using multiple instance learning density matrices," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 2, pp. 526-541, Apr. 2011.
    • (2011) IEEE Trans. Syst., Man, Cybern. B, Cybern. , vol.41 , Issue.2 , pp. 526-541
    • Kelly, D.1    McDonald, J.2    Markham, C.3
  • 15
    • 80955134248 scopus 로고    scopus 로고
    • Multi-instance multi-label learning
    • Z. Zhou, M. Zhang, S. Huang, and Y. Li, "Multi-instance multi-label learning," Artif. Intell., vol. 176, no. 1, pp. 2291-2320, 2012.
    • (2012) Artif. Intell. , vol.176 , Issue.1 , pp. 2291-2320
    • Zhou, Z.1    Zhang, M.2    Huang, S.3    Li, Y.4
  • 16
    • 0141596676 scopus 로고    scopus 로고
    • Solving the multiple-instance problem: A lazy learning approach
    • San Francisco, CA, USA
    • J. Wang, "Solving the multiple-instance problem: A lazy learning approach," in Proc. 17th ICML, San Francisco, CA, USA, 2000, pp. 1119-1125.
    • (2000) Proc. 17th ICML , pp. 1119-1125
    • Wang, J.1
  • 17
    • 31844431728 scopus 로고    scopus 로고
    • Multi-instance tree learning
    • Bonn, Germany
    • H. Blockeel and A. Srinivasan, "Multi-instance tree learning," in Proc. 22th ICML, Bonn, Germany, 2005, pp. 57-64.
    • (2005) Proc. 22th ICML , pp. 57-64
    • Blockeel, H.1    Srinivasan, A.2
  • 18
    • 83755194948 scopus 로고    scopus 로고
    • Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data
    • Berlin, Heidelberg
    • L. Bjerring and E. Frank, "Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data," in Proc. 24th Int. Conf. Adv. AI, Berlin, Heidelberg, 2011, pp. 41-50.
    • (2011) Proc. 24th Int. Conf. Adv. AI , pp. 41-50
    • Bjerring, L.1    Frank, E.2
  • 19
    • 84948152022 scopus 로고    scopus 로고
    • A framework for learning rules from multiple instance data
    • Freiburg, Germany
    • Y. Chevaleyre and J. Zucker, "A framework for learning rules from multiple instance data," in Proc. 12th ECML, Freiburg, Germany, 2001, pp. 49-60.
    • (2001) Proc. 12th ECML , pp. 49-60
    • Chevaleyre, Y.1    Zucker, J.2
  • 20
    • 1642357513 scopus 로고    scopus 로고
    • Improve multi-instance neural networks through feature selection
    • M. Zhang and Z. Zhou, "Improve multi-instance neural networks through feature selection," Neural Process. Lett., vol. 19, no. 1, pp. 1-10, 2004.
    • (2004) Neural Process. Lett. , vol.19 , Issue.1 , pp. 1-10
    • Zhang, M.1    Zhou, Z.2
  • 21
    • 33750284913 scopus 로고    scopus 로고
    • Incorporating multiple SVMs for automatic image annotation
    • X. Qi and Y. Han, "Incorporating multiple SVMs for automatic image annotation," Pattern Recogn., vol. 40, no. 2, pp. 728-741, 2007.
    • (2007) Pattern Recogn. , vol.40 , Issue.2 , pp. 728-741
    • Qi, X.1    Han, Y.2
  • 22
    • 31844448950 scopus 로고    scopus 로고
    • Supervised versus multiple instance learning: An empirical comparison
    • New York, NY, USA
    • S. Ray and M. Craven, "Supervised versus multiple instance learning: An empirical comparison," in Proc. 22nd ICML, New York, NY, USA, 2005, pp. 697-704.
    • (2005) Proc. 22nd ICML , pp. 697-704
    • Ray, S.1    Craven, M.2
  • 23
    • 84887963348 scopus 로고    scopus 로고
    • Hierarchical sampling for multi-instance ensemble learning
    • Dec.
    • H. Yuan, M. Fang, and X. Zhu, "Hierarchical sampling for multi-instance ensemble learning," IEEE Trans. Knowl. Data Eng., vol. 25, no. 12, pp. 2900-2905, Dec. 2013.
    • (2013) IEEE Trans. Knowl. Data Eng. , vol.25 , Issue.12 , pp. 2900-2905
    • Yuan, H.1    Fang, M.2    Zhu, X.3
  • 24
    • 7444219637 scopus 로고    scopus 로고
    • Logistic regression and boosting for labeled bags of instances
    • X. Xu and E. Frank, "Logistic regression and boosting for labeled bags of instances," in Proc. 8th PAKDD, 2004, pp. 272-281.
    • (2004) Proc. 8th PAKDD , pp. 272-281
    • Xu, X.1    Frank, E.2
  • 25
    • 84859456417 scopus 로고    scopus 로고
    • A primal-dual convergence analysis of boosting
    • M. Telgarsky, "A primal-dual convergence analysis of boosting," J. Mach. Learn. Res., vol. 13, no. 1, pp. 561-606, 2012.
    • (2012) J. Mach. Learn. Res. , vol.13 , Issue.1 , pp. 561-606
    • Telgarsky, M.1
  • 26
    • 84898935332 scopus 로고    scopus 로고
    • A framework for multiple-instance learning
    • Cambridge, MA, USA
    • O. Maron and T. Lozano-Pérez, "A framework for multiple-instance learning," in Proc. 12th Annu. Conf. NIPS, Cambridge, MA, USA, 1998, pp. 570-576.
    • (1998) Proc. 12th Annu. Conf. NIPS , pp. 570-576
    • Maron, O.1    Lozano-Pérez, T.2
  • 27
    • 84898999828 scopus 로고    scopus 로고
    • EM-DD: An improved multiple-instance learning technique
    • Q. Zhang and S. Goldman, "EM-DD: An improved multiple-instance learning technique," in Proc. 15th Annu. Conf. NIPS, 2001, pp. 1073-1080.
    • (2001) Proc. 15th Annu. Conf. NIPS , pp. 1073-1080
    • Zhang, Q.1    Goldman, S.2
  • 28
    • 22944460788 scopus 로고    scopus 로고
    • A boosting approach to multiple instance learning
    • Pisa, Italy
    • P. Auer and R. Ortner, "A boosting approach to multiple instance learning," in Proc. 15th ECML, Pisa, Italy, 2004, pp. 63-74.
    • (2004) Proc. 15th ECML , pp. 63-74
    • Auer, P.1    Ortner, R.2
  • 29
    • 84894683682 scopus 로고    scopus 로고
    • Multi-instance multi-graph dual embedding learning
    • Dallas, TX, USA
    • J. Wu, X. Zhu, C. Zhang, and Z. Cai, "Multi-instance multi-graph dual embedding learning," in Proc. 13th ICDM, Dallas, TX, USA, 2013, pp. 827-836.
    • (2013) Proc. 13th ICDM , pp. 827-836
    • Wu, J.1    Zhu, X.2    Zhang, C.3    Cai, Z.4
  • 31
    • 14344252908 scopus 로고    scopus 로고
    • Extensions of marginalized graph kernels
    • New York, NY, USA
    • P. Mahe, N. Ueda, T. Akutsu, J. Pettet, and J. Vert, "Extensions of marginalized graph kernels," in Proc. 21st ICML, New York, NY, USA, 2004, pp. 552-559.
    • (2004) Proc. 21st ICML , pp. 552-559
    • Mahe, P.1    Ueda, N.2    Akutsu, T.3    Pettet, J.4    Vert, J.5
  • 32
    • 70349621824 scopus 로고    scopus 로고
    • Graph classification by means of Lipschitz embedding
    • Dec.
    • K. Riesen and H. Bunke, "Graph classification by means of Lipschitz embedding," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 6, pp. 1472-1483, Dec. 2009.
    • (2009) IEEE Trans. Syst., Man, Cybern. B, Cybern. , vol.39 , Issue.6 , pp. 1472-1483
    • Riesen, K.1    Bunke, H.2
  • 34
    • 78149333073 scopus 로고    scopus 로고
    • GSpan: Graph-based substructure pattern mining
    • Washington, DC, USA
    • X. Yan and J. Han, "gSpan: Graph-based substructure pattern mining," in Proc. 2nd ICDM, Washington, DC, USA, 2002, pp. 721-724.
    • (2002) Proc. 2nd ICDM , pp. 721-724
    • Yan, X.1    Han, J.2
  • 35
    • 84974733299 scopus 로고    scopus 로고
    • An apriori-based algorithm for mining frequent substructures from graph data
    • Lyon, France
    • A. Inokuchi, T. Washio, and H. Motoda, "An apriori-based algorithm for mining frequent substructures from graph data," in Proc. 4th Eur. Conf. PKDD, Lyon, France, 2000, pp. 13-23.
    • (2000) Proc. 4th Eur. Conf. PKDD , pp. 13-23
    • Inokuchi, A.1    Washio, T.2    Motoda, H.3
  • 36
    • 2442483205 scopus 로고    scopus 로고
    • Mining molecular fragments: Finding relevant substructures of molecules
    • C. Borgelt and M. Berthold, "Mining molecular fragments: Finding relevant substructures of molecules," in Proc. 2nd ICDM, 2002, pp. 51-58.
    • (2002) Proc. 2nd ICDM , pp. 51-58
    • Borgelt, C.1    Berthold, M.2
  • 37
    • 12244294066 scopus 로고    scopus 로고
    • A quickstart in frequent structure mining can make a difference
    • Seattle, WA, USA
    • S. Nijssen and J. Kok, "A quickstart in frequent structure mining can make a difference," in Proc. 10th ACM SIGKDD, Seattle, WA, USA, 2004, pp. 647-652.
    • (2004) Proc. 10th ACM SIGKDD , pp. 647-652
    • Nijssen, S.1    Kok, J.2
  • 38
    • 57149124218 scopus 로고    scopus 로고
    • Mining significant graph patterns by leap search
    • Vancouver, BC, Canada
    • X. Yan, H. Cheng, J. Han, and P. S. Yu, "Mining significant graph patterns by leap search," in Proc. 27th ACM SIGMOD, Vancouver, BC, Canada, 2008, pp. 433-444.
    • (2008) Proc. 27th ACM SIGMOD , pp. 433-444
    • Yan, X.1    Cheng, H.2    Han, J.3    Yu, P.S.4
  • 39
    • 65449142148 scopus 로고    scopus 로고
    • Partial least squares regression for graph mining
    • Las Vegas, NV, USA
    • H. Saigo, N. Krämer, and K. Tsuda, "Partial least squares regression for graph mining," in Proc. 14th ACM SIGKDD, Las Vegas, NV, USA, 2008, pp. 578-586.
    • (2008) Proc. 14th ACM SIGKDD , pp. 578-586
    • Saigo, H.1    Krämer, N.2    Tsuda, K.3
  • 40
    • 77954691039 scopus 로고    scopus 로고
    • GAIA: Graph classification using evolutionary computation
    • Indianapolis, IN, USA
    • N. Jin, C. Young, and W. Wang, "GAIA: Graph classification using evolutionary computation," in Proc. 29th ACM SIGMOD, Indianapolis, IN, USA, 2010, pp. 879-890.
    • (2010) Proc. 29th ACM SIGMOD , pp. 879-890
    • Jin, N.1    Young, C.2    Wang, W.3
  • 41
  • 43
    • 60949105177 scopus 로고    scopus 로고
    • GBoost: A mathematical programming approach to graph classification and regression
    • H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda, "gBoost: A mathematical programming approach to graph classification and regression," Mach. Learn., vol. 75, no. 1, pp. 69-89, 2009.
    • (2009) Mach. Learn. , vol.75 , Issue.1 , pp. 69-89
    • Saigo, H.1    Nowozin, S.2    Kadowaki, T.3    Kudo, T.4    Tsuda, K.5
  • 44
    • 84896063101 scopus 로고    scopus 로고
    • Graph classification with imbalanced class distributions and noise
    • S. Pan and X. Zhu, "Graph classification with imbalanced class distributions and noise," in Proc. 23rd IJCAI, 2013, pp. 1586-1592.
    • (2013) Proc. 23rd IJCAI , pp. 1586-1592
    • Pan, S.1    Zhu, X.2
  • 45
    • 77956212133 scopus 로고    scopus 로고
    • Boosting with structure information in the functional space: An application to graph classification
    • Washington, DC, USA
    • H. Fei and J. Huan, "Boosting with structure information in the functional space: An application to graph classification," in Proc. 16th ACM SIGKDD, Washington, DC, USA, 2010, pp. 643-652.
    • (2010) Proc. 16th ACM SIGKDD , pp. 643-652
    • Fei, H.1    Huan, J.2
  • 46
    • 78149471648 scopus 로고    scopus 로고
    • Multi-class graph boosting with subgraph sharing for object recognition
    • Istanbul, Turkey
    • B. Zhang et al., "Multi-class graph boosting with subgraph sharing for object recognition," in Proc. 20th ICPR, Istanbul, Turkey, 2010, pp. 1541-1544.
    • (2010) Proc. 20th ICPR , pp. 1541-1544
    • Zhang, B.1
  • 47
    • 84868267952 scopus 로고    scopus 로고
    • Sparse principal component analysis with constraints
    • M. Grbovic, C. Dance, and S. Vucetic, "Sparse principal component analysis with constraints," in Proc. 26th Conf. AAAI, 2012, pp. 935-941.
    • (2012) Proc. 26th Conf. AAAI , pp. 935-941
    • Grbovic, M.1    Dance, C.2    Vucetic, S.3
  • 48
    • 34547972773 scopus 로고    scopus 로고
    • Boosting for transfer learning
    • Corvallis, OR, USA
    • W. Dai, Q. Yang, G. Xue, and Y. Yu, "Boosting for transfer learning," in Proc. 24th ICML, Corvallis, OR, USA, 2007, pp. 193-200.
    • (2007) Proc. 24th ICML , pp. 193-200
    • Dai, W.1    Yang, Q.2    Xue, G.3    Yu, Y.4
  • 49
    • 65449166085 scopus 로고    scopus 로고
    • ArnetMiner: Extraction and mining of academic social networks
    • Las Vegas, NV, USA
    • J. Tang et al., "ArnetMiner: Extraction and mining of academic social networks," in Proc. 14th ACM SIGKDD, Las Vegas, NV, USA, 2008, pp. 990-998.
    • (2008) Proc. 14th ACM SIGKDD , pp. 990-998
    • Tang, J.1
  • 50
    • 33750616759 scopus 로고    scopus 로고
    • Using fuzzy cognitive maps for knowledge management in a conflict environment
    • Nov.
    • K. Perusich and M. McNeese, "Using fuzzy cognitive maps for knowledge management in a conflict environment," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 36, no. 6, pp. 810-821, Nov. 2006.
    • (2006) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. , vol.36 , Issue.6 , pp. 810-821
    • Perusich, K.1    McNeese, M.2
  • 51
    • 53349153768 scopus 로고    scopus 로고
    • Discovery of textual knowledge flow based on the management of knowledge maps
    • X. L. Q. Hu, W. Xu, and Z. Yu, "Discovery of textual knowledge flow based on the management of knowledge maps," Concurr. Comput. Pract. Exp., vol. 20, no. 15, pp. 1791-1806, 2008.
    • (2008) Concurr. Comput. Pract. Exp. , vol.20 , Issue.15 , pp. 1791-1806
    • Hu, X.L.Q.1    Xu, W.2    Yu, Z.3
  • 52
    • 79960112691 scopus 로고    scopus 로고
    • Building association link network for semantic link on web resources
    • Jul.
    • X. Luo, Z. Xu, J. Yu, and X. Chen, "Building association link network for semantic link on web resources," IEEE Trans. Autom. Sci. Eng., vol. 8, no. 3, pp. 482-494, Jul. 2011.
    • (2011) IEEE Trans. Autom. Sci. Eng. , vol.8 , Issue.3 , pp. 482-494
    • Luo, X.1    Xu, Z.2    Yu, J.3    Chen, X.4
  • 53
    • 84936938071 scopus 로고    scopus 로고
    • Multi-graph learning with positive and unlabeled bags
    • J. Wu et al., "Multi-graph learning with positive and unlabeled bags," in Proc. 14th SIAM Int. Conf. Data Mining, 2014, pp. 217-225.
    • (2014) Proc. 14th SIAM Int. Conf. Data Mining , pp. 217-225
    • Wu, J.1
  • 54
    • 85028138882 scopus 로고    scopus 로고
    • Bag constrained structure pattern mining for multi-graph classification
    • to be published
    • J. Wu, X. Zhu, C. Zhang, and P. Yu, "Bag constrained structure pattern mining for multi-graph classification," IEEE Trans. Knowl. Data Eng., to be published.
    • IEEE Trans. Knowl. Data Eng
    • Wu, J.1    Zhu, X.2    Zhang, C.3    Yu, P.4
  • 57
    • 0036161257 scopus 로고    scopus 로고
    • Linear programming boosting via column generation
    • A. Demiriz, K. P. Bennett, and J. Shawe-Taylor, "Linear programming boosting via column generation," Mach. Learn., vol. 46, no. 1-3, pp. 225-254, 2002.
    • (2002) Mach. Learn. , vol.46 , Issue.1-3 , pp. 225-254
    • Demiriz, A.1    Bennett, K.P.2    Shawe-Taylor, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.