289
Views
12
CrossRef citations to date
0
Altmetric
Original Articles

ENSEMBLING REGRESSION MODELS TO IMPROVE THEIR PREDICTIVITY: A CASE STUDY IN QSAR (QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS) WITH COMPUTATIONAL CHEMOMETRICS

, &
Pages 261-281 | Published online: 12 Mar 2009

REFERENCES

  • Avnimelech , R. and N. Intrator . 1999 . Boosted mixture of experts: An ensemble learning scheme . Neural Computation 11 : 483 – 497 .
  • Bauer , E. and R. Kohavi . 1999 . An empirical comparison of voting classification algorithms: Bagging, boosting, and variants . Machine Learning 36 ( 1–2 ): 105 – 139 .
  • Benfenati , E. Ed. 2007 . Quantitative Structure-Activity Relationships (QSAR) for Pesticides Regulatory Purposes . Philadelphia : Elsevier .
  • Benfenati , E. , J. R. Chretien , G. Gini , N. Piclin , M. Pintore , and A. Roncaglioni . 2007 . Validation of the models . In Quantitative Structure-Activity Relationship (QSAR) for Pesticide Regulatory Purposes , pp. 187 – 189 , Amsterdam : Elsevier .
  • Benfenati , E. , P. Mazzatorta , D. Neagu , and G. Gini . 2002 . Combining classifiers of pesticides toxicity through a neuro-fuzzy approach . In Multiple Classifier Systems, Lecture Notes in Computer Science 2364 , Springer , 293 – 303 .
  • Bi , J. and K. P. Bennett . 2003 . Regression error characteristic curves. Procs. 20th International Conference on Machine Learning (ICML-2003), Washington, DC. .
  • Breiman , L. 1996 . Bagging predictors . Machine Learning 24 ( 2 ): 123 – 140 .
  • Chen , S. H. and P. P. Wang . eds. 2004 . Computational Intelligence in Economics and Finance . Berlin : Springer-Verlag .
  • d'Avila Garcez , A. S. , K. Broda , and D. M. Gabbay . 2002 . Neural-symbolic learning systems: Foundations and applications. Perspectives in Neural Computing . Berlin : Springer-Verlag .
  • Dietterich , T. 2000 . Ensemble methods in machine learning . In Multiple Classifier Systems—1st Int. Workshop, MCS 2000, 1857, Lecture Notes in Computer Science , eds. J. Kittler , and F. Roli , Cagliari , Italy , pp. 1 – 15 .
  • Freund , Y. , M. Yishay , and R. E. Schapire . 2004 . Generalization bounds for averaged classifiers . Annals of Statistics 32 : 1698 – 1722 .
  • Friedman , J. 1997 . On bias, variance, 0/1 loss and the curse of dimensionality . Data Mining Knowledge Discovery 1 : 55 – 77 .
  • Funahashi , K. 1989. On the approximate realization of continuous mappings by neural networks. Neural Networks 2:183–192.
  • Gallant , S. I. 1993 . Neural Network Learning and Expert Systems . Cambridge , MA : MIT Press .
  • Gini , G. and A. Katrizky (eds.) 1999 . Predictive toxicology of chemicals: Experiences and impact of AI tools. AAAI Spring Symposium on Predictive Toxicology SS-99-01 . Menlo Park , CA : American Association for Artificial Intelligence Press .
  • Gini , G. , M. Craciun , C. Koening , and E. Benfenati . 2004 . Combining unsupervised and supervised artificial neural networks to predict aquatic toxicity . J. Chemical Information and Computer Sciences (The American Chemical Society) 44(6):1897–1902. .
  • Gini , G. , M. Lorenzini , E. Benfenati , R. Brambilla , and L. Malvé . 2001 . Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromaticcompounds. Lecture Notes in Computer Science LNCS 2096 , Berlin : Springer-Verlag .
  • Golbraikh , A. and A. Tropsha . 2002 . Beware of q2! J. Mol. Graph Model 20 : 269 – 276 .
  • Hansch , C. , P. P. Malony , T. Fujita , and R. M. Muir . 1962 . Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants with partition coefficents . Nature , 194 : 178 – 180 .
  • Helma , C. and S. Kramer . 2003 . A survey of the Predictive Toxicology Challenge 2000–2001 . Bioinformatics 19 ( 10 ): 1179 – 1182 .
  • Ho , T. K. 2002 . multiple classifier combination: Lessons and next steps . In Hybrid Methods in Pattern Recognition , eds. A. Kandel , and H. Bunke . World Scientific 2002. .
  • Ho , T. K. , J. J. Hull , and S. N. Srihari . 1994 . Decision combination in multiple classifier systems . IEEE Transactions on Pattern Analysis and Machine Intelligence 16 ( 1 ): 66 – 75 .
  • Jackson , P. 1999 . Introduction to Expert Systems, 3rd ed. Harlow, UK: Addison Wesley Longman. .
  • Jacob , R. A. , M. I. Jordan , S. J. Nowlan , and G. E. Hinton . 1991 . Adaptive mixtures of local experts . Neural Computation 3 : 79 – 87 .
  • Kittler , J. M. , R. Hatef , R. Duin , and J. Matas . 1998 . On combining classifiers . IEEE Trans. Pattern Analysis and Machine Intelligence 20 ( 3 ): 226 – 239 .
  • Koening , C. , G. Gini , M. Craciun , and E. Benfenati . 2004 . Multi-class classifier from a combination of local experts: Toward distributed computation for real-problem classifiers . Int. J. Pattern Recognition and Artificial Intelligence 18 ( 5 ): 801 – 817 .
  • Krogh , A. and J. Vedelsby . 1995 . Neural network ensembles, cross validation and active learning . In Advances in Neural Information Processing Systems , eds. G. Tesauro , D. S. Touretzky , and T. K. Leen , Cambridge , MA : MIT Press .
  • Merkwirth , C. , H. Mauser , T. Schulz-Gasch , O. Roche , and T. Lengauerý . 2004 . Ensemble methods for classification in cheminformatics . J. Chem. Inf. Comput. Sci. 44 : 1971 – 1978 .
  • Meyer , H. 1899 . Naunyn Schmiedebergs . Arch. Exp. Path. Pharm. 42 : 109 – 118 .
  • Neagu , C. -D. and G. Gini . 2003 . Neuro-fuzzy knowledge integration applied to toxicity prediction . In: Innovations in Knowledge Engineering , eds. R. Jain , A. Abraham , C. Faucher , and B. Jan van der Zwaag , Advanced Knowledge International Pty Ltd, Ad, Australia. .
  • Quinlan , J. R. 1993 . C4.5: Programs for Machine Learning . Los Altos , CA , USA : Morgan Kauffman .
  • Toivonen , H. , A. Srinivasan , R. D. King , S. Kramer , and C. Helma . 2003 . Statistical evaluation of the predictive toxicology challenge 2000–2001 . Bioinformatics 19 ( 10 ): 1183 – 1193 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.