Title: Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis

Authors: Chao Y. Ma, Frances V. Buontempo, Xue Z. Wang

Addresses: Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds LS2 9JT, UK. ' Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds LS2 9JT, UK. ' Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds LS2 9JT, UK

Abstract: A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretisation prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated that uses multiple decision trees, i.e., a decision forest, and a majority voting strategy to give predictions (GPForest). The methods were applied to QSAR (quantitative structure – activity relationships) modelling for eco-toxicity prediction of chemicals and to the analysis of a historical database for a wastewater treatment plant.

Keywords: inductive data mining; decision trees; genetic programming; quantitative structure activity relationships; QSAR; process historical data analysis; wastewater treatment; modelling; eco-toxicity prediction.

DOI: 10.1504/IJMIC.2011.037837

International Journal of Modelling, Identification and Control, 2011 Vol.12 No.1/2, pp.101 - 106

Available online: 31 Dec 2010 *

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