Random Forest for Bioinformatics

Qi, Yanjun

doi:10.1007/978-1-4419-9326-7_11

Cited by 568 publications

(392 citation statements)

References 48 publications

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“…Random forests (RF; Breiman 2001) are widely used in applications, such as gene expression analysis, protein-protein interactions, identification of biological sequences, genome-wide association studies, credit scoring or image processing (Bosch, Zisserman, and Muoz 2007;Kruppa, Schwarz, Arminger, and Ziegler 2013;Qi 2012). Random forests have been described in several review articles, and a recent one with links to other review papers has been provided by Ziegler and König (2014).…”

Section: Introductionmentioning

confidence: 99%

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

Wright¹,

Ziegler²

2017

J. Stat. Soft.

2,371

1,690

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We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.

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Section: Introductionmentioning

confidence: 99%

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

Wright¹,

Ziegler²

2017

J. Stat. Soft.

2,371

1,690

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show abstract

“…In [19] we proposed an intrusion detection system model based on K-star and Information gain for feature set reduction. The key idea of this paper is to take advantage of the instance-based classifier and dataset features reduction for intrusion detection system, the model has the ability to recognize attacks with high detection rate and low false negative.…”

Section: Related Work and Methodsmentioning

confidence: 99%

An Experimental Study of Diabetes Disease Prediction System Using Classification Techniques

Tamilvanan¹,

Bhaskaran²

2017

IOSR JCE

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“…Random Forest (RF) [10,11,12] is an ensemble classification and regression trees (CARTs) that are grown in a random subspace of data. Each tree in the forest is grown using a bootstrap sample of instances from the data.…”

Section: Machine Learningmentioning

confidence: 99%

A Machine Learning Based Approach for Detecting Non-Deterministic Tests and Its Analysis in Mobile Application Testing

Bhojan

Ramyachitra²,

Vivekanandan³

et al. 2018

ijarcs

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Abstract:Hundreds of different mobile devices are on the market, produced by different vendors, and with different software features and hardware components. Mobile applications, while running on different devices, may behave differently due to variations in the hardware or O.S. components. Since mobile applications are expected to be deployed and executed on diverse mobile platforms, they must be validated on different mobile platforms and devices. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Moreover, mobile test executions take long time because all the tests were executed on different environments and developers had to create complex tear down procedures. Such procedures were lengthy and far from perfect, leading to unpredictable failures. Regression testing is a crucial part of Mobile app development and it checks that software changes do not break existing functionality. In every regression test execution, the final results are expected either always pass or always fail for the same code. But, in real time project release cycles, some of the tests will be non-deterministic, in other words called flaky tests. It reduces the importance of regression testing cycle and it is very difficult to trust on these results. These results significantly reduced the trust in the tests and thus undermined the whole mobile app test automation effort. We trained machine learning classifiers separately on each test result dataset and compared performance across datasets. The proposed model predicts result types as Non-Deterministic orDeterministic tests from the regression suite results executed in various release cycles.

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Random Forest for Bioinformatics

Cited by 568 publications

References 48 publications

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

An Experimental Study of Diabetes Disease Prediction System Using Classification Techniques

A Machine Learning Based Approach for Detecting Non-Deterministic Tests and Its Analysis in Mobile Application Testing

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