Using ensembles of decision trees to automate repetitive tasks in web applications

Bray, Zachary; Kristensson, Per Ola

doi:10.1145/1822018.1822025

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The CI techniques that were explored for the reduced-component hybrid models were taken from those with promising capabilities as reported in the literature. (Bray and Kristensson 2010), extreme learning machines (ELMs) (Heeswijk et al 2009;Avci and Coteli 2012), and SVM (Sewell 2008;Vapnik 2000). The proposed hybrid models include FN-SVM, DT-SVM, fuzzy ranking-SVM, FN-T2FLS, and FN-ELM.…”

Section: Hybrid Intelligent Systems In Petroleum Reservoir Characterimentioning

confidence: 99%

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

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Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.

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Section: Hybrid Intelligent Systems In Petroleum Reservoir Characterimentioning

confidence: 99%

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

Anifowose

Labadin

Abdulraheem

2015

Applied Soft Computing

127

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a b s t r a c tThe ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.

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Using ensembles of decision trees to automate repetitive tasks in web applications

Cited by 2 publications

References 14 publications

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

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