Random forest for ordinal responses: Prediction and variable selection

Janitza, Silke; Tutz, Gerhard; Boulesteix, Anne‐Laure

doi:10.1016/j.csda.2015.10.005

Cited by 171 publications

(100 citation statements)

References 24 publications

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“…The second metric measures the mean decrease in accuracy in the OOB sample by randomly permuting the predictor variable of interest (see randomForest R package, [16]). VIMs for the case of ordinal response variables have also been proposed in Janitza et al [72].…”

Section: Variable Importance Metricsmentioning

confidence: 99%

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Tyralis

Papacharalampous

Langousis

2019

Water

461

243

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Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.

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Section: Variable Importance Metricsmentioning

confidence: 99%

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Tyralis

Papacharalampous

Langousis

2019

Water

461

243

View full text Add to dashboard Cite

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“…The details of these variables and their computed techniques are given in Table 2. If the number of variables is too large, as is often the case for multi-source studies, a RF can be applied only to those variables that have been identified as the most important and that contribute most to increased accuracy [45,53]. As a result, we calibrated our model based on a complete dataset, then used the variable importance results to tune parameter settings such as increasing the number of mtry.…”

Section: Collection Of Reference Data and Variable Selectionmentioning

confidence: 99%

“…In Random Forest a cross-validation test is estimated internally during the bootstrapping process as 70% of training data are used for the tree growing process and the remaining 30% are used to estimate OOB error [42,53]. As a result, separate validation does not get an unbiased estimate of the test error [49].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Hassan

Southworth

2017

Sustainability

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Accurate information on, and human interpretation of, urban land cover using satellite-derived sensor imagery is critical given the intricate nature and niches of socioeconomic, demographic, and environmental factors occurring at multiple temporal and spatial scales. Detailed knowledge of urban land and their changing pattern over time periods associated with ecological risk is, however, required for the best use of critical land and its environmental resources. Interest in this topic has increased recently, driven by a surge in the use of open-source computing software, satellite-derived imagery, and improved classification algorithms. Using the machine learning algorithm Random Forest, combined with multi-date Landsat imagery, we classified eight periods of land cover maps with up-to-date spatial and temporal information of urban land between the period of 1972 and 2015 for the mega-urban region of greater Dhaka in Bangladesh. Random Forest-a non-parametric ensemble classifier-has shown a quantum increase in satellite-derived image classification accuracy due to its outperformance over traditional approaches, e.g., Maximum Likelihood. Employing Random Forest as an image classification approach for this study with independent cross-validation techniques, we obtained high classification accuracy, user and producer accuracy. Our overall classification accuracy ranges were between 85% and 97% with kappa values between 0.81 and 0.94. The area statistics derived from the thematic land cover map show that the built-up area in the 43-year study period expanded quickly, from 35 km 2 in 1972 to 378 km 2 in 2015, with a net increase rate of approximately 980% and an average annual growth rate of 6%. This growth rate, however, was higher in peripheral areas, with a 2903% increase and an annual expansion rate of 8%, compared to a 460% increase with an annual growth rate of 4% in the core city area (Dhaka City Corporation). This huge urban expansion took place in the north, northwest, and southwest regions of Dhaka, transforming areas that were previously agricultural land, vegetation cover, wetland, and water bodies. The main factors driving the city towards northern corridors include flood-free higher land, the availability of a transportation network, and the agglomeration of manufacturing-based employment centers. The resulting thematic map and spatial information produced from this study therefore serve to facilitate a detailed understanding of urban growth dynamics and land cover change patterns in the mega-urban region of Dhaka, Bangladesh.

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“…The exploitation of this stored data, in order to extract useful and practical information, is the general purpose of the public activity known as data mining. Data mining the process of discovery and analysis, either automatically or semi-automatic, is of greatly high value in order to discover meaningful patterns and rules [8,9]. Data mining is an interdisciplinary branch of computer science that involves the discovery of patterns from a large set of data.…”

Section: Data Collection and Classificationmentioning

confidence: 99%

Use of data mining in the corrosion classification of pipelines in catalytic reforming units (CRU)

Samimi

Zarinabadi

Kotanaei

et al. 2020

Eurasian Chem. Commun.

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Nowadays, computational tools for analyzing and collecting data in the operation of petroleum units are essential. One of the methods is the classification or regression is to step in the overall process of knowledge extraction. In this study, one of specific type of decision called the conditional contract arrangement, is used to extract the relevant knowledge in Catalytic Reforming Units (CRU) for 4 factors: Density, PH, total iron ions in vessels (S.FE) and H2S. All of these factors are related to corrosion in CRU and this paper aims to optimize some conditions to eliminate it. In this regard, using ammonium water with a specific range and PH can be helpful. According to the obtained results the best range of density (in Feed) is less than 0.515 kg/m 3 , PH (water in vessels) is more than 6.7, S.FE is less than 1.5 ppm and H2S in recycle gas is less than 700 ppm. The outcomes also show how this approach can be used to gain insight into some refineries and how to deliver results in a comprehensible and user-friendly way.

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Random forest for ordinal responses: Prediction and variable selection

Cited by 171 publications

References 24 publications

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Use of data mining in the corrosion classification of pipelines in catalytic reforming units (CRU)

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