-
1
-
-
84861432903
-
-
Alaydie, N., Reddy, C.K., Fotouhi, F. (2012). Exploiting label dependency for hierarchical multi-label classification. (pp. 294–305). Berlin: Heidelberg, New York: Springer
-
Alaydie, N., Reddy, C.K., Fotouhi, F. (2012). Exploiting label dependency for hierarchical multi-label classification. In Proceedings of the 16th Pacific-Asia conference on advances in knowledge discovery and data mining (pp. 294–305). Berlin: Heidelberg, New York: Springer.
-
Proceedings of the 16th Pacific-Asia conference on advances in knowledge discovery and data mining
-
-
-
2
-
-
36849072723
-
-
Cambridge, MA: The MIT Press
-
Bakır, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N. (Eds.) (2007). Predicting structured data. Cambridge, MA: The MIT Press.
-
(2007)
Predicting structured data
-
-
Bakır, G.H.1
Hofmann, T.2
Schölkopf, B.3
Smola, A.J.4
Taskar, B.5
Vishwanathan, S.V.N.6
-
3
-
-
84886544524
-
-
Berlin Heidelberg: Springer
-
Barros, R.C., Cerri, R., Freitas, A.A., de Carvalho, A.C.P.L.F. (2013). Probabilistic clustering for hierarchical multi-label classification of protein functions. In H. Blockeel, K. Kersting, S. Nijssen, F. železný (Eds.), Machine learning and knowledge discovery in databases, Lecture Notes in Computer Science, (Vol. 8189 pp. 385–400). Berlin Heidelberg: Springer.
-
(2013)
Probabilistic clustering for hierarchical multi-label classification of protein functions. In H. Blockeel, K. Kersting, S. Nijssen, F. železný (Eds.), Machine learning and knowledge discovery in databases, Lecture Notes in Computer Science, (Vol. 8189 pp. 385–400)
-
-
Barros, R.C.1
Cerri, R.2
Freitas, A.A.3
de Carvalho, A.C.P.L.F.4
-
4
-
-
33645323768
-
Hierarchical multi-label prediction of gene function
-
Barutcuoglu, Z., Schapire, R.E., Troyanskaya, O.G. (2006). Hierarchical multi-label prediction of gene function. Bioinformatics, 22(7), 830–836.
-
(2006)
Bioinformatics
, vol.22
, Issue.7
, pp. 830-836
-
-
Barutcuoglu, Z.1
Schapire, R.E.2
Troyanskaya, O.G.3
-
5
-
-
0032645080
-
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
-
Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1), 105–139.
-
(1999)
Machine Learning
, vol.36
, Issue.1
, pp. 105-139
-
-
Bauer, E.1
Kohavi, R.2
-
7
-
-
2542439351
-
Top-down induction of first order logical decision trees. Ph.D. thesis
-
Leuven, Belgium
-
Blockeel, H. (1998). Top-down induction of first order logical decision trees. Ph.D. thesis, Katholieke Universiteit Leuven, Leuven, Belgium.
-
(1998)
Katholieke Universiteit Leuven
-
-
Blockeel, H.1
-
8
-
-
11244325978
-
Hierarchical multi-classification
-
Blockeel, H., Bruynooghe, M., Džeroski, S., Ramon, J., Struyf, J. (2002). Hierarchical multi-classification. In Proceedings of the ACM SIGKDD workshop on multi-relational data mining (pp. 21–35).
-
(2002)
In Proceedings of the ACM SIGKDD workshop on multi-relational data mining
, pp. 21-35
-
-
Blockeel, H.1
Bruynooghe, M.2
Džeroski, S.3
Ramon, J.4
Struyf, J.5
-
9
-
-
33750303563
-
-
Blockeel, H., Schietgat, L., Struyf, J., Džeroski, S., Clare, A. (2006). Decision trees for hierarchical multilabel classification: A case study in functional genomics (Vol. 4213 pp. 18–29). Berlin Heidelberg: Springer
-
Blockeel, H., Schietgat, L., Struyf, J., Džeroski, S., Clare, A. (2006). Decision trees for hierarchical multilabel classification: A case study in functional genomics. In Knowledge discovery in databases: PKDD, Lecture Notes in Computer Science (Vol. 4213 pp. 18–29). Berlin Heidelberg: Springer.
-
Knowledge discovery in databases: PKDD, Lecture Notes in Computer Science
-
-
-
10
-
-
0141496153
-
Efficient algorithms for decision tree cross-validation
-
Blockeel, H., & Struyf, J. (2002). Efficient algorithms for decision tree cross-validation. Journal of Machine Learning Research, 3, 621–650.
-
(2002)
Journal of Machine Learning Research
, vol.3
, pp. 621-650
-
-
Blockeel, H.1
Struyf, J.2
-
11
-
-
0030211964
-
Bagging predictors
-
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
-
(1996)
Machine Learning
, vol.24
, Issue.2
, pp. 123-140
-
-
Breiman, L.1
-
12
-
-
0035478854
-
Random forests
-
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
-
(2001)
Machine Learning
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
13
-
-
0003802343
-
-
Chapman & Hall/CRC, London, UK
-
Breiman, L., Friedman, J., Olshen, R.A., Stone, C.J. (1984). Classification and regression trees. London, UK: Chapman & Hall/CRC.
-
(1984)
Classification and regression trees
-
-
Breiman, L.1
Friedman, J.2
Olshen, R.A.3
Stone, C.J.4
-
15
-
-
84884973606
-
Hierarchical multi-label classification using local neural networks
-
Cerri, R., Barros, R.C., de Carvalho, A.C.P.L.F. (2014). Hierarchical multi-label classification using local neural networks. Journal of Computer and System Sciences, 80(1), 39–56.
-
(2014)
Journal of Computer and System Sciences
, vol.80
, Issue.1
, pp. 39-56
-
-
Cerri, R.1
Barros, R.C.2
de Carvalho, A.C.P.L.F.3
-
16
-
-
25844463253
-
Machine learning and data mining for yeast functional genomics. Ph.D. thesis
-
Aberystwyth, UK
-
Clare, A. (2003). Machine learning and data mining for yeast functional genomics. Ph.D. thesis, University of Wales Aberystwyth, Aberystwyth, UK.
-
(2003)
University of Wales Aberystwyth
-
-
Clare, A.1
-
17
-
-
6944251719
-
Predicting gene function in Saccharomyces cerevisiae
-
Clare, A., & King, R.D. (2003). Predicting gene function in Saccharomyces cerevisiae. Bioinformatics, 19(S2), ii42–49.
-
(2003)
Bioinformatics
, vol.19
, Issue.S2
, pp. ii42-ii49
-
-
Clare, A.1
King, R.D.2
-
19
-
-
28444472696
-
Using multi-objective classification to model communities of soil
-
Demšar, D., Džeroski, S., Larsen, T., Struyf, J., Axelsen, J., Bruns-Pedersen, M., Krogh, P.H. (2006). Using multi-objective classification to model communities of soil. Ecological Modelling, 191(1), 131–143.
-
(2006)
Ecological Modelling
, vol.191
, Issue.1
, pp. 131-143
-
-
Demšar, D.1
Džeroski, S.2
Larsen, T.3
Struyf, J.4
Axelsen, J.5
Bruns-Pedersen, M.6
Krogh, P.H.7
-
20
-
-
29644438050
-
Statistical comparisons of classifiers over multiple data sets
-
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1-30
-
-
Demšar, J.1
-
21
-
-
50649102327
-
Structured machine learning: The next ten years
-
Dietterich, T.G., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P. (2008). Structured machine learning: The next ten years. Machine Learning, 73(1), 3–23.
-
(2008)
Machine Learning
, vol.73
, Issue.1
, pp. 3-23
-
-
Dietterich, T.G.1
Domingos, P.2
Getoor, L.3
Muggleton, S.4
Tadepalli, P.5
-
22
-
-
84941939564
-
-
Dimitrovski, I., Kocev, D., Loskovska, S., Džeroski, S. (2008). Hierchical annotation of medical images, (pp. 174–181). Ljubljana:JSI
-
Dimitrovski, I., Kocev, D., Loskovska, S., Džeroski, S. (2008). Hierchical annotation of medical images. In Proceedings of the 11th international multiconference - information society (pp. 174–181). Ljubljana:JSI.
-
Proceedings of the 11th international multiconference - information society
-
-
-
23
-
-
70549096553
-
Machine learning applications in habitat suitability modeling
-
In Artificial intelligence methods in the environmental sciences, Springer Netherlands
-
Džeroski, S. (2009). Machine learning applications in habitat suitability modeling. In: S.E. Haupt, A. Pasini, C. Marzban (Eds.) In Artificial intelligence methods in the environmental sciences. Springer Netherlands, (pp. 397–412).
-
(2009)
S.E
, pp. 397-412
-
-
Džeroski, S.1
Haupt, A.P.2
Marzban, C.3
-
24
-
-
0343391218
-
Predicting chemical parameters of river water quality from bioindicator data
-
Džeroski, S., Demšar, D., Grbović, J. (2000). Predicting chemical parameters of river water quality from bioindicator data. Applied Intelligence, 13(1), 7–17.
-
(2000)
Applied Intelligence
, vol.13
, Issue.1
, pp. 7-17
-
-
Džeroski, S.1
Demšar, D.2
Grbović, J.3
-
25
-
-
33646379108
-
Web categorisation using distance-based decision trees
-
Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J. (2006). Web categorisation using distance-based decision trees. Electronic Notes in Theoretical Computer Science, 157(2), 35–40.
-
(2006)
Electronic Notes in Theoretical Computer Science
, vol.157
, Issue.2
, pp. 35-40
-
-
Estruch, V.1
Ferri, C.2
Hernández-Orallo, J.3
Ramírez-Quintana, M.J.4
-
26
-
-
47549108100
-
Predicting gene function in a hierarchical context with an ensemble of classifiers
-
Guan, Y., Myers, C.L., Hess, D.C., Barutcuoglu, Z., Caudy, A., Troyanskaya, O. (2008). Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology, 9(S1), S3+.
-
(2008)
Genome Biology
, vol.9
, Issue.S1
, pp. S3+
-
-
Guan, Y.1
Myers, C.L.2
Hess, D.C.3
Barutcuoglu, Z.4
Caudy, A.5
Troyanskaya, O.6
-
27
-
-
33746058639
-
-
Berlin Heidelberg: Springer
-
Kiritchenko, S., Famili, F., Matwin, S., Nock, R. (2006). Learning and evaluation in the presence of class hierarchies: Application to text categorization. In L. Lamontagne, M. Marchand (Eds.), Advances in artificial intelligence, Lecture Notes in Computer Science, (Vol. 4013 pp. 395–406). Berlin Heidelberg: Springer.
-
(2006)
Learning and evaluation in the presence of class hierarchies: Application to text categorization. In L. Lamontagne, M. Marchand (Eds.), Advances in artificial intelligence, Lecture Notes in Computer Science, (Vol. 4013 pp. 395–406)
-
-
Kiritchenko, S.1
Famili, F.2
Matwin, S.3
Nock, R.4
-
28
-
-
22944464423
-
-
Berlin Heidelberg: Springer
-
Klimt, B., & Yang, Y. (2004). The enron corpus: A new dataset for email classification research. In J.F. Boulicaut, F. Esposito, F. Giannotti, D. Pedreschi (Eds.), Machine learning: ECML, Lecture Notes in Computer Science, (Vol. 3201 pp. 217–226). Berlin Heidelberg: Springer.
-
(2004)
The enron corpus: A new dataset for email classification research. In J.F. Boulicaut, F. Esposito, F. Giannotti, D. Pedreschi (Eds.), Machine learning: ECML, Lecture Notes in Computer Science, (Vol. 3201 pp. 217–226)
-
-
Klimt, B.1
Yang, Y.2
-
29
-
-
84870255848
-
Tree ensembles for predicting structured outputs
-
Kocev, D., Vens, C., Struyf, J., Džeroski, S. (2013). Tree ensembles for predicting structured outputs. Pattern Recognition, 46(3), 817–833.
-
(2013)
Pattern Recognition
, vol.46
, Issue.3
, pp. 817-833
-
-
Kocev, D.1
Vens, C.2
Struyf, J.3
Džeroski, S.4
-
30
-
-
34547275841
-
Future trends in data mining
-
Kriegel, H.P., Borgwardt, K., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A. (2007). Future trends in data mining. Data Mining and Knowledge Discovery, 15, 87–97.
-
(2007)
Data Mining and Knowledge Discovery
, vol.15
, pp. 87-97
-
-
Kriegel, H.P.1
Borgwardt, K.2
Kröger, P.3
Pryakhin, A.4
Schubert, M.5
Zimek, A.6
-
31
-
-
0042421547
-
The IRMA code for unique classification of medical images
-
Lehmann, T., Schubert, H., Keysers, D., Kohnen, M., Wein, B. (2003). The IRMA code for unique classification of medical images. In Medical imaging: PACS and integrated medical information systems: Design and evaluation (pp. 440–451).
-
(2003)
In Medical imaging: PACS and integrated medical information systems: Design and evaluation
, pp. 440-451
-
-
Lehmann, T.1
Schubert, H.2
Keysers, D.3
Kohnen, M.4
Wein, B.5
-
33
-
-
84905259010
-
The use of the label hierarchy in hierarchical multi-label classification improves performance
-
Levatić, J., Kocev, D., Džeroski, S. (2014). The use of the label hierarchy in hierarchical multi-label classification improves performance. In A. Appice, et al. (Eds.), New frontiers in mining complex patterns, Lecture Notes in Computer Science, (Vol. 8399 pp. 1–16): Springer International Publishing.
-
(2014)
New frontiers in mining complex patterns, Lecture Notes in Computer Science
-
-
Levatić, J.1
Kocev, D.2
Džeroski, S.3
-
34
-
-
84876811202
-
RCV1: A new benchmark collection for text categorization research
-
Lewis, D.D., Yang, Y., Rose, T.G., Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361–397.
-
(2004)
Journal of Machine Learning Research
, vol.5
, pp. 361-397
-
-
Lewis, D.D.1
Yang, Y.2
Rose, T.G.3
Li, F.4
-
35
-
-
47549088657
-
Consistent probabilistic outputs for protein function prediction
-
Obozinski, G., Lanckriet, G., Grant, C., Jordan, M.I., Noble, W.S. (2008). Consistent probabilistic outputs for protein function prediction. Genome Biology, 9(S1), S6+.
-
(2008)
Genome Biology
, vol.9
, Issue.S1
, pp. S6+
-
-
Obozinski, G.1
Lanckriet, G.2
Grant, C.3
Jordan, M.I.4
Noble, W.S.5
-
36
-
-
77956692717
-
A hierarchical multi-label classification ant colony algorithm for protein function prediction
-
Otero, F.E., Freitas, A.A., Johnson, C.G. (2010). A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Computing, 2(3), 165–181.
-
(2010)
Memetic Computing
, vol.2
, Issue.3
, pp. 165-181
-
-
Otero, F.E.1
Freitas, A.A.2
Johnson, C.G.3
-
38
-
-
33745768424
-
Kernel-based learning of hierarchical multilabel classification models
-
Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J. (2006). Kernel-based learning of hierarchical multilabel classification models. The Journal of Machine Learning Research, 7, 1601–1626.
-
(2006)
The Journal of Machine Learning Research
, vol.7
, pp. 1601-1626
-
-
Rousu, J.1
Saunders, C.2
Szedmak, S.3
Shawe-Taylor, J.4
-
39
-
-
9144257282
-
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes
-
Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M., et al. (2004). The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research, 32(18), 5539–5545.
-
(2004)
Nucleic Acids Research
, vol.32
, Issue.18
, pp. 5539-5545
-
-
Ruepp, A.1
Zollner, A.2
Maier, D.3
Albermann, K.4
Hani, J.5
Mokrejs, M.6
Tetko, I.7
Güldener, U.8
Mannhaupt, G.9
Münsterkötter, M.10
-
40
-
-
77349119213
-
Predicting gene function using hierarchical multi-label decision tree ensembles
-
Schietgat, L., Vens, C., Struyf, J., Blockeel, H., Kocev, D., Džeroski, S. (2010). Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinformatics, 11(2), 1–14.
-
(2010)
BMC Bioinformatics
, vol.11
, Issue.2
, pp. 1-14
-
-
Schietgat, L.1
Vens, C.2
Struyf, J.3
Blockeel, H.4
Kocev, D.5
Džeroski, S.6
-
42
-
-
78651375098
-
A survey of hierarchical classification across different application domains
-
Silla, C., & Freitas, A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22(1-2), 31–72.
-
(2011)
Data Mining and Knowledge Discovery
, vol.22
, Issue.1-2
, pp. 31-72
-
-
Silla, C.1
Freitas, A.2
-
44
-
-
77949701475
-
Finding explained groups of time-course gene expression profiles with predictive clustering trees
-
Slavkov, I., Gjorgjioski, V., Struyf, J., Džeroski, S. (2010). Finding explained groups of time-course gene expression profiles with predictive clustering trees. Molecular BioSystems, 6(4), 729–740.
-
(2010)
Molecular BioSystems
, vol.6
, Issue.4
, pp. 729-740
-
-
Slavkov, I.1
Gjorgjioski, V.2
Struyf, J.3
Džeroski, S.4
-
46
-
-
52949141834
-
Decision trees for hierarchical multi-label classification
-
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 73(2), 185–214.
-
(2008)
Machine Learning
, vol.73
, Issue.2
, pp. 185-214
-
-
Vens, C.1
Struyf, J.2
Schietgat, L.3
Džeroski, S.4
Blockeel, H.5
|