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Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

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Department of Chemical Engineering and School of Civil Engineering, University of Leeds, Leeds LS2 9JT, U.K., AstraZeneca UK Ltd., Brixham Environmental Laboratory, Freshwater Quarry, Brixham, Devon TQ5 8BA, U.K., and Centre of Ecology and Hydrology, Monks Wood, Huntingdon PE28 2LS, U.K.
Cite this: J. Chem. Inf. Model. 2005, 45, 4, 904–912
Publication Date (Web):May 12, 2005
https://doi.org/10.1021/ci049652n
Copyright © 2005 American Chemical Society

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    Abstract

    Automatic induction of decision trees and production rules from data to develop structure−activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.

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     Department of Chemical Engineering, University of Leeds.

    *

     Corresponding author phone:  +44 113 343 2427; fax:  +44 113 343 2405; e-mail:  [email protected].

     School of Civil Engineering, University of Leeds.

    §

     AstraZeneca UK Ltd.

     Centre of Ecology and Hydrology.

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    7. Ceyda Oksel, Cai Y. Ma, Jing J. Liu, Terry Wilkins, Xue Z. Wang. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity. 2017, 103-142. https://doi.org/10.1007/978-3-319-47754-1_5
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    11. Philip Judson. The Application of Structure–Activity Relationships to the Prediction of the Mutagenic Activity of Chemicals. 2012, 1-19. https://doi.org/10.1007/978-1-61779-421-6_1
    12. Yang Yang, Tian Lin, Xiao L. Weng, Jawwad A. Darr, Xue Z. Wang. Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials. Computers & Chemical Engineering 2011, 35 (4) , 671-678. https://doi.org/10.1016/j.compchemeng.2010.04.018
    13. Chao Y. Ma, Xue Z. Wang. Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis. Computers & Chemical Engineering 2009, 33 (10) , 1602-1616. https://doi.org/10.1016/j.compchemeng.2009.04.005
    14. . Bibliography. 2009, 1-241. https://doi.org/10.1002/9783527628766.biblio
    15. Chao Y Ma, Frances V Buontempo, Xue Z Wang. Inductive data mining: Automatic generation of decision trees from data for QSAR modelling and process historical data analysis. 2008, 581-586. https://doi.org/10.1016/S1570-7946(08)80102-2
    16. Paul Watson. Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors. Journal of Chemical Information and Modeling 2008, 48 (1) , 166-178. https://doi.org/10.1021/ci7003253
    17. X. Z. Wang, F. V. Buontempo, A. Young, D. Osborn. Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints. SAR and QSAR in Environmental Research 2006, 17 (5) , 451-471. https://doi.org/10.1080/10629360600933723
    18. M. Mwense, X. Z. Wang, F. V. Buontempo, N. Horan, A. Young, D. Osborn. QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions†. SAR and QSAR in Environmental Research 2006, 17 (1) , 53-73. https://doi.org/10.1080/10659360600562202

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