메뉴 건너뛰기




Volumn 17, Issue 3, 2008, Pages 287-312

Multirelational classification: A multiple view approach

Author keywords

Classification; Ensemble; Multi view learning; Multirelational data mining; Relational database

Indexed keywords

DATA MINING; LEARNING ALGORITHMS; LEARNING SYSTEMS; RELATIONAL DATABASE SYSTEMS;

EID: 57149108333     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0127-5     Document Type: Article
Times cited : (40)

References (77)
  • 1
    • 3543091484 scopus 로고    scopus 로고
    • On leveraging user access patterns for topic specific crawling
    • Aggarwal CC (2004). On leveraging user access patterns for topic specific crawling. Data Min Knowl Discov 9(2): 123-145
    • (2004) Data Min Knowl Discov , vol.9 , Issue.2 , pp. 123-145
    • Aggarwal, C.C.1
  • 3
    • 9444271884 scopus 로고    scopus 로고
    • Guide to the financial data set
    • In: Siebes A, Berka P (eds)
    • Berka P (2000) Guide to the financial data set. In: Siebes A, Berka P (eds) PKDD2000 discovery challenge
    • (2000) PKDD2000 Discovery Challenge
    • Berka, P.1
  • 6
    • 0032069371 scopus 로고    scopus 로고
    • Top-Down Induction of First-Order Logical Decision Trees
    • Blockeel H and Raedt LD (1998). Top-Down Induction of First-Order Logical Decision Trees. Artif Intell 101(1-2): 285-297
    • (1998) Artif Intell , vol.101 , Issue.1-2 , pp. 285-297
    • Blockeel, H.1    Raedt, L.D.2
  • 7
    • 22644449312 scopus 로고    scopus 로고
    • Scaling up inductive logic programming by learning from interpretations
    • Blockeel H, Raedt LD, Jacobs N and Demoen B (1999). Scaling up inductive logic programming by learning from interpretations. Data Min Knowl Discov 3(1): 59-93
    • (1999) Data Min Knowl Discov , vol.3 , Issue.1 , pp. 59-93
    • Blockeel, H.1    Raedt, L.D.2    Jacobs, N.3    Demoen, B.4
  • 9
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L (1996). Bagging predictors. Mach Learn 24(2): 123-140
    • (1996) Mach Learn , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 10
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges CJC (1998). A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2): 121-167
    • (1998) Data Mining Knowl Discov , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 11
    • 85132067551 scopus 로고    scopus 로고
    • Collective mining of Bayesian networks from distributed heterogeneous data
    • Chen R, Sivakumar K and Kargupta H (2004). Collective mining of Bayesian networks from distributed heterogeneous data. Knowl Inf Syst 6(2): 164-187
    • (2004) Knowl Inf Syst , vol.6 , Issue.2 , pp. 164-187
    • Chen, R.1    Sivakumar, K.2    Kargupta, H.3
  • 12
    • 27944488680 scopus 로고    scopus 로고
    • Accurate prediction of protein disordered regions by mining protein structure data
    • Cheng J, Sweredoski MJ and Baldi P (2005). Accurate prediction of protein disordered regions by mining protein structure data. Data Min Knowl Discov 11(3): 213-222
    • (2005) Data Min Knowl Discov , vol.11 , Issue.3 , pp. 213-222
    • Cheng, J.1    Sweredoski, M.J.2    Baldi, P.3
  • 13
    • 0030379749 scopus 로고    scopus 로고
    • Efficient mining of association rules in distributed databases
    • Cheung DW, Ng VT, Fu AW and Fu Y (1996). Efficient mining of association rules in distributed databases. IEEE Trans Knowl Data Eng 8(6): 911-922
    • (1996) IEEE Trans Knowl Data Eng , vol.8 , Issue.6 , pp. 911-922
    • Cheung, D.W.1    Ng, V.T.2    Fu, A.W.3    Fu, Y.4
  • 14
    • 0042965713 scopus 로고    scopus 로고
    • Distributed mining of classification rules
    • Cho V and Wüthrich B (2002). Distributed mining of classification rules. Knowl Inf Syst 4(1): 1-30
    • (2002) Knowl Inf Syst , vol.4 , Issue.1 , pp. 1-30
    • Cho, V.1    Wüthrich, B.2
  • 15
    • 34249966007 scopus 로고
    • The CN2 induction algorithm
    • Clark P and Niblett T (1989). The CN2 induction algorithm. Mach Learn 3(4): 261-283
    • (1989) Mach Learn , vol.3 , Issue.4 , pp. 261-283
    • Clark, P.1    Niblett, T.2
  • 18
    • 14844283227 scopus 로고    scopus 로고
    • PAC generalization bounds for co-training
    • Dasgupta S, Littman ML, McAllester DA (2001) PAC generalization bounds for co-training. In: NIPS, pp 375-382
    • (2001) NIPS , pp. 375-382
    • Dasgupta, S.1    Littman, M.L.2    McAllester, D.A.3
  • 19
    • 0032111421 scopus 로고    scopus 로고
    • Category learning through multi-modality sensing
    • de Sa VR and Ballard DH (1998). Category learning through multi-modality sensing. Neural Comput 10(5): 1097-1117
    • (1998) Neural Comput , vol.10 , Issue.5 , pp. 1097-1117
    • de Sa, V.R.1    Ballard, D.H.2
  • 20
    • 0344324689 scopus 로고    scopus 로고
    • MetaCost: A general method for making classifiers cost-Sensitive
    • Domingos P (1999) MetaCost: A general method for making classifiers cost-Sensitive. In: KDD'99, pp 155-164
    • (1999) KDD'99 , pp. 155-164
    • Domingos, P.1
  • 22
    • 6344279447 scopus 로고    scopus 로고
    • Multi-relational data mining: An introduction
    • Dzeroski S and Raedt LD (2003). Multi-relational data mining: An introduction. SIGKDD Explor Newsl 5(1): 1-16
    • (2003) SIGKDD Explor Newsl , vol.5 , Issue.1 , pp. 1-16
    • Dzeroski, S.1    Raedt, L.D.2
  • 25
    • 23044519492 scopus 로고    scopus 로고
    • RainForest - A framework for fast decision tree construction of large datasets
    • Gehrke J, Ramakrishnan R and Ganti V (2000). RainForest - a framework for fast decision tree construction of large datasets. Data Min Knowl Discov 4(2-3): 127-162
    • (2000) Data Min Knowl Discov , vol.4 , Issue.2-3 , pp. 127-162
    • Gehrke, J.1    Ramakrishnan, R.2    Ganti, V.3
  • 30
    • 27144479454 scopus 로고    scopus 로고
    • Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach
    • Guo H and Viktor HL (2004). Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach. SIGKDD Explor Newsl 6(1): 30-39
    • (2004) SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 30-39
    • Guo, H.1    Viktor, H.L.2
  • 32
    • 33749560103 scopus 로고    scopus 로고
    • Mining relational data through correlation-based multiple view validation
    • In: ACM Press, New York
    • Guo H, Viktor HL (2006) Mining relational data through correlation-based multiple view validation. In: KDD '06. ACM Press, New York, pp 567-573
    • (2006) KDD '06 , pp. 567-573
    • Guo, H.1    Viktor, H.L.2
  • 33
    • 0004060921 scopus 로고    scopus 로고
    • Correlation-based feature selection for machine learning
    • Ph.D dissertation Waikato University
    • Hall M (1998) Correlation-based feature selection for machine learning. Ph.D dissertation Waikato University
    • (1998)
    • Hall, M.1
  • 36
    • 57149107654 scopus 로고    scopus 로고
    • Support vector machines (Aktuelles Schlagwort)
    • Joachims T (1999). Support vector machines (Aktuelles Schlagwort). KI 13(4): 54-55
    • (1999) KI , vol.13 , Issue.4 , pp. 54-55
    • Joachims, T.1
  • 37
    • 0000468432 scopus 로고
    • Estimating continuous distributions in Bayesian classifiers
    • John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: UAI, pp 338-345
    • (1995) UAI , pp. 338-345
    • John, G.H.1    Langley, P.2
  • 38
    • 85132267044 scopus 로고    scopus 로고
    • Distributed clustering using collective principal component analysis
    • Kargupta H, Huang W, Sivakumar K and Johnson E (2001). Distributed clustering using collective principal component analysis. Knowl Inf Syst 3(4): 422-448
    • (2001) Knowl Inf Syst , vol.3 , Issue.4 , pp. 422-448
    • Kargupta, H.1    Huang, W.2    Sivakumar, K.3    Johnson, E.4
  • 39
    • 26944482842 scopus 로고    scopus 로고
    • MINING MART: Combining case-based-reasoning and multistrategy learning into a framework for reusing KDD-applications
    • In: Guimaraes, Portugal
    • Kietz J-U, Zücker R, Vaduva A (2000) MINING MART: Combining case-based-reasoning and multistrategy learning into a framework for reusing KDD-applications. In: 5th Int'l workshop on multistrategy learning (MSL 2000). Guimaraes, Portugal
    • (2000) 5th Int' Workshop on Multistrategy Learning (MSL 2000)
    • Kietz, J.-U.1    Zücker, R.2    Vaduva, A.3
  • 40
    • 33646394250 scopus 로고    scopus 로고
    • Multi-relational data mining
    • Ph.D. thesis, University Utrecht
    • Knobbe AJ (2004) Multi-relational data mining. Ph.D. thesis, University Utrecht
    • (2004)
    • Knobbe, A.J.1
  • 42
    • 0003763626 scopus 로고
    • Wrappers for performance enhancement and oblivious decision graphs
    • Ph.D. thesis, Stanford University
    • Kohavi R (1995) Wrappers for performance enhancement and oblivious decision graphs. Ph.D. thesis, Stanford University
    • (1995)
    • Kohavi, R.1
  • 43
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi R and John GH (1997). Wrappers for feature subset selection. Artif Intell 97(1-2): 273-324
    • (1997) Artif Intell , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 44
    • 33751471188 scopus 로고    scopus 로고
    • On propositionalization for knowledge discovery in relational databases
    • Ph.D. thesis, Fakultät fuer Informatik, Otto-von-Guericke-Universität Magdeburg
    • Krogel M-A (2005) On propositionalization for knowledge discovery in relational databases. Ph.D. thesis, Fakultät fuer Informatik, Otto-von-Guericke-Universität Magdeburg
    • (2005)
    • Krogel, M.-A.1
  • 46
    • 84937420049 scopus 로고    scopus 로고
    • Transformation-based learning using multirelational aggregation
    • Krogel M-A, Wrobel S (2001) Transformation-based learning using multirelational aggregation. In: ILP, pp 142-155
    • (2001) ILP , pp. 142-155
    • Krogel, M.-A.1    Wrobel, S.2
  • 49
    • 57149092014 scopus 로고
    • Principles of knowledge acquisition in expert systems
    • Ph.D. thesis, Faculty of Technical Sciences, University of Maribor
    • Lavrač N (1990) Principles of knowledge acquisition in expert systems. Ph.D. thesis, Faculty of Technical Sciences, University of Maribor
    • (1990)
    • Lavrač, N.1
  • 50
    • 85005299854 scopus 로고
    • The multi-purpose incremental learning system AQ15 and its testing application to three medical domains
    • Michalski RS, Mozetic I, Hong J, Lavrac N (1986) The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In: AAAI, pp 1041-1047
    • (1986) AAAI , pp. 1041-1047
    • Michalski, R.S.1    Mozetic, I.2    Hong, J.3    Lavrac, N.4
  • 53
    • 0028429573 scopus 로고
    • Inductive logic programming: Theory and methods
    • Muggleton S and Raedt LD (1994). Inductive logic programming: Theory and methods. J Log Programm 19/20: 629-679
    • (1994) J Log Programm , vol.19-20 , pp. 629-679
    • Muggleton, S.1    Raedt, L.D.2
  • 54
    • 22544476130 scopus 로고    scopus 로고
    • Active learning with multiple views
    • Ph.D. thesis, Department of Computer Science, University of Southern California
    • Muslea IA (2002) Active learning with multiple views. Ph.D. thesis, Department of Computer Science, University of Southern California
    • (2002)
    • Muslea, I.A.1
  • 55
    • 77952399122 scopus 로고    scopus 로고
    • Learning relational probability trees
    • In: ACM Press, New York
    • Neville J, Jensen D, Friedland L, Hay M (2003) Learning relational probability trees. In: KDD '03. pp 625-630, ACM Press, New York
    • (2003) KDD '03 , pp. 625-630
    • Neville, J.1    Jensen, D.2    Friedland, L.3    Hay, M.4
  • 56
    • 85132288287 scopus 로고    scopus 로고
    • Parallel data mining for association rules on shared-memory systems
    • Parthasarathy S, Zaki MJ, Ogihara M and Li W (2001). Parallel data mining for association rules on shared-memory systems. Knowl Inf Syst 3(1): 1-29
    • (2001) Knowl Inf Syst , vol.3 , Issue.1 , pp. 1-29
    • Parthasarathy, S.1    Zaki, M.J.2    Ogihara, M.3    Li, W.4
  • 57
    • 22944446870 scopus 로고    scopus 로고
    • Aggregation-based feature invention and relational concept classes
    • Perlich C, Provost FJ (2003) Aggregation-based feature invention and relational concept classes. In: KDD'03, pp 167-176
    • (2003) KDD'03 , pp. 167-176
    • Perlich, C.1    Provost, F.J.2
  • 64
    • 0012433134 scopus 로고    scopus 로고
    • Distributed web log mining using maximal large itemsets
    • Sayal M and Scheuermann P (2001). Distributed web log mining using maximal large itemsets. Knowl Inf Syst 3(4): 389-404
    • (2001) Knowl Inf Syst , vol.3 , Issue.4 , pp. 389-404
    • Sayal, M.1    Scheuermann, P.2
  • 65
    • 0012457092 scopus 로고    scopus 로고
    • Parallel and sequential algorithms for data mining using inductive logic
    • Skillicorn DB and Wang Y (2001). Parallel and sequential algorithms for data mining using inductive logic. Knowl Inf Syst 3(4): 405-421
    • (2001) Knowl Inf Syst , vol.3 , Issue.4 , pp. 405-421
    • Skillicorn, D.B.1    Wang, Y.2
  • 66
    • 22644452635 scopus 로고    scopus 로고
    • Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes
    • Srinivasan A and King RD (1999). Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Min Knowl Discov 3(1): 37-57
    • (1999) Data Min Knowl Discov , vol.3 , Issue.1 , pp. 37-57
    • Srinivasan, A.1    King, R.D.2
  • 67
    • 0030212927 scopus 로고    scopus 로고
    • Theories for mutagenicity: A study in first-order and feature-based induction
    • Srinivasan A, Muggleton SH, Sternberg MJE and King RD (1996). Theories for mutagenicity: A study in first-order and feature-based induction. Artif Intell 85(1-2): 277-299
    • (1996) Artif Intell , vol.85 , Issue.1-2 , pp. 277-299
    • Srinivasan, A.1    Muggleton, S.H.2    Sternberg, M.J.E.3    King, R.D.4
  • 68
    • 1942418618 scopus 로고    scopus 로고
    • Discriminative probabilistic models for relational data
    • Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: UAI, pp 485-492
    • (2002) UAI , pp. 485-492
    • Taskar, B.1    Abbeel, P.2    Koller, D.3
  • 69
    • 22944433186 scopus 로고    scopus 로고
    • First order random forests with complex aggregates
    • Vens C, Assche AV, Blockeel H, Dzeroski S (2004) First order random forests with complex aggregates. In: ILP, pp 323-340
    • (2004) ILP , pp. 323-340
    • Vens, C.1    Assche, A.V.2    Blockeel, H.3    Dzeroski, S.4
  • 70
    • 4344706336 scopus 로고    scopus 로고
    • Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques
    • Webb G and Zheng Z (2004). Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques. IEEE Trans Knowl Data Eng 16(8): 980-991
    • (2004) IEEE Trans Knowl Data Eng , vol.16 , Issue.8 , pp. 980-991
    • Webb, G.1    Zheng, Z.2
  • 71
    • 0034247206 scopus 로고    scopus 로고
    • MultiBoosting: A technique for combining boosting and bagging
    • Webb GI (2000). MultiBoosting: A technique for combining boosting and bagging. Mach Learn 40(2): 159-196
    • (2000) Mach Learn , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 73
    • 0026692226 scopus 로고
    • Stacked generalization
    • Wolpert DH (1992). Stacked generalization. Neural Netw 5(2): 241-259
    • (1992) Neural Netw , vol.5 , Issue.2 , pp. 241-259
    • Wolpert, D.H.1
  • 74
    • 4644356822 scopus 로고    scopus 로고
    • Database classification for multi-database mining
    • Wu X, Zhang C and Zhang S (2005). Database classification for multi-database mining. Inf Syst 30(1): 71-88
    • (2005) Inf Syst , vol.30 , Issue.1 , pp. 71-88
    • Wu, X.1    Zhang, C.2    Zhang, S.3
  • 75
    • 0037339975 scopus 로고    scopus 로고
    • Synthesizing high-frequency rules from different data sources
    • Wu X and Zhang S (2003). Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 15(2): 353-367
    • (2003) IEEE Trans Knowl Data Eng , vol.15 , Issue.2 , pp. 353-367
    • Wu, X.1    Zhang, S.2
  • 76
    • 2442458705 scopus 로고    scopus 로고
    • CrossMine: Efficient classification across multiple database relations
    • In: Boston
    • Yin X, Han J, Yang J, Yu PS (2004) CrossMine: Efficient classification across multiple database relations. In: ICDE'04, Boston, pp 399-410
    • (2004) ICDE'04 , pp. 399-410
    • Yin, X.1    Han, J.2    Yang, J.3    Yu, P.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.