Structure damage detection based on random forest recursive feature elimination

Zhou, Qifeng; Zhou, Hao; Zhou, Qingqing; Yang, Fan; Luo, Linkai

doi:10.1016/j.ymssp.2013.12.013

Cited by 76 publications

(40 citation statements)

References 17 publications

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“…It is built by bootstrap sampling technology and random splitting technology, and the final classification result is made by a majority vote of the trees [39,40]. Because of its excellent generalization performance, RF is also further used for feature selection [41,42].…”

Section: Feature Selection With Random Forestmentioning

confidence: 99%

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Qiao

Lan

et al. 2019

Applied Sciences

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Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.

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Section: Feature Selection With Random Forestmentioning

confidence: 99%

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Qiao

Lan

et al. 2019

Applied Sciences

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“…Based on the RMSE values, one variable was removed, and a new RF model was created using the remaining variables. This process was recursively applied until only one variable remained as input [36]. During the process of elimination, 10-fold cross validation was implemented to optimize the variable selection and to ascertain the standard RF is a collection of several decision trees where each tree is constructed independently with random samples (n) from the training data.…”

Section: Introductionmentioning

confidence: 99%

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

2018

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Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R 2 CV (cross-validated coefficient of determination) = 0.70, RMSE CV (cross-validated root mean square error) = 2.06%, RPD CV (cross-validated ratio to prediction deviation) = 1.82 and ME: R 2 CV = 0.75, RMSE CV = 0.65 MJ/kg DM, RPD CV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R 2 CV = 0.80, RMSE CV = 1.68%, RPD CV = 2.23; ME: R 2 CV = 0.78, RMSE CV = 0.61 MJ/kg DM, RPD CV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits.

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“…To reduce the set of 27 candidate predictor variables, a “random forest recursive feature elimination” was applied. The algorithm starts from the complete set and then eliminates the least relevant variables one after the other based on a variable importance ranking produced by fitting random forests (Guyon, Weston, Barnhill, & Vapnik, ; Zhou, Zhou, Zhou, Yang, & Luo, ). A logistic regression model was then calibrated using the retained variables and the possible connecting segments from Section 3.3.2 that were classified based on field data.…”

Section: Methodsmentioning

confidence: 99%

Extracting drainage networks and their connectivity using LiDAR data

Roelens

Rosier

Dondeyne

et al. 2018

Hydrological Processes

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Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north-eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.

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Structure damage detection based on random forest recursive feature elimination

Cited by 76 publications

References 17 publications

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

Extracting drainage networks and their connectivity using LiDAR data

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