Genetic Neural Networks for Functional Approximation

Chiu, Ting-Lan; So, Sung‐Sau

doi:10.1002/qsar.200310004

Cited by 11 publications

(3 citation statements)

References 42 publications

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“…The option to employ elitism 45 was added. This carries the best user-specified number of trees through to the next generation.…”

Section: The Methodsmentioning

confidence: 99%

“…Genetic algorithms have been used in decision tree generation, to decide the splitting points and attributes to be used while growing a tree. , In contrast, EPTree uses evolutionary programming, sometimes called genetic programming to explore combinations of splitting nodes forming the decision trees themselves. This approach has all the benefits of genetic algorithms, including simultaneous investigation of many solutions and the avoidance of local minima, but does not require parameter encoding into fixed length vectors called chromosomes. Furthermore, the direct action on the decision trees themselves avoids some of the problems usually associated with decision tree induction from genetic programming, such as the tendency to grow overly large trees, known as bloat. , …”

Section: Introductionmentioning

confidence: 99%

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

Buontempo

Wang

Mwense

et al. 2005

J. Chem. Inf. Model.

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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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“…The option to employ elitism 45 was added. This carries the best user-specified number of trees through to the next generation.…”

Section: The Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Buontempo

Wang

Mwense

et al. 2005

J. Chem. Inf. Model.

View full text Add to dashboard Cite

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“…In order to implement this rule we used fixed number of input neurons [4,7]. According to another rule of thumb used by Chiu and So [39] and Patankar and Jurs [10] the number of training objects in this case molecules should be approximately twice the number of NN adjustable parameters. Similar or even more demanding rules could be found in NN literature [36].…”

Section: Nn Implementationmentioning

confidence: 99%

Novel approach to evolutionary neural network based descriptor selection and QSAR model development

Debeljak¹,

Marohnic²,

Srečnik

et al. 2006

J Comput Aided Mol Des

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Capability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.

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2009

Molecular Descriptors for Chemoinformatics

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Genetic Neural Networks for Functional Approximation

Cited by 11 publications

References 42 publications

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

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

Novel approach to evolutionary neural network based descriptor selection and QSAR model development

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