Optimizing feature sets for structured data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4701 LNAI, Issue , 2007, Pages 716-723

  Rückert, Ulrich ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMBINATORIAL OPTIMIZATION; DATA STRUCTURES; LINEAR SYSTEMS; PROBLEM SOLVING;

FEATURE SETS; STOCHASTIC LOCAL SEARCH (SLS);

FEATURE EXTRACTION;

EID: 38049113105     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74958-5_72     Document Type: Conference Paper

References (11)

1. Borgelt, C.: On canonical forms for frequent graph mining. In: Proc. 3rd Int. Workshop on Mining Graphs, Trees, and Sequences, pp. 1-12 (2005)
2. Bringmann, B., Zimmermann, A., De Raedt, L., Nijssen, S.: Don't be afraid of simpler patterns. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 55-66. Springer, Heidelberg (2006)
3. Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructurebased approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering 17(8), 1036-1050 (2005)
4. Fang, H., Tong, W., Shi, L.M., Blair, R., Perkins, R., Branham, W., Hass, B.S., Xie, Q., Dial, S.L., Moland, C.L., Sheehan, D.M.: Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chemical Research in Toxicology 14(3), 280-294 (2001)
5. Fröhlich, H., Wegner, J.K., Sieker, F., Zell, A.: Optimal assignment kernels for attributed molecular graphs. In: Proceedings of the 22nd ICML, pp. 225-232. ACM Press, New York (2005)
6. Landwehr, N., Passerini, A., De Raedt, L., Frasconi, P.: kFOIL: Learning simple relational kernels. In: AAAI, AAAI Press (2006)
7. Li, H., Yap, CW., Ung, C.Y., Xue, Y., Cao, Z.W., Chen, Y.Z.: Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. Journal of Chemical Information and Modeling 45(5), 1376-1384 (2005)
8. Rückert, U., Kramer, S.: Stochastic local search in k-term DNF learning. In: Proceedings of the 20th ICML, pp. 648-655. AAAI Press (2003)
9. Rückert, U., Kramer, S.: A statistical approach to rule learning. In: Proceedings of the 23rd ICML, pp. 785-792. ACM Press, New York (2006)
10. Yoshida, F., Topliss, J.: QSAR model for drug human oral bioavailability. J. Med. Chem. 43, 2575-2585 (2000)
11. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205-1224 (2004)