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Utgoff, Paul E.; Berkman, Neil C.; Clouse, Jeffery A.

doi:10.1023/a:1007413323501

Cited by 265 publications

(9 citation statements)

References 20 publications

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“…The regression tree-based approaches, belonging to supervised learning, are widely taken to SM simulation and estimation since their inception (Han et al, 2018;Im et al, 2016;Wei et al, 2019). Basically, regression tree approach has a tree structure, in which each internal node represents a judgment on attribute, each branch represents an output of fitting model, and each leaf node represents a prediction result (Utgoff et al, 1997). Among the four methods, the CART gets the highest computational efficiency.…”

Section: Methodsmentioning

confidence: 99%

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

Liu

Xia

Yao

et al. 2020

Earth and Space Science

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Satellite retrieved soil moisture (SM) shows great potential in hydrological, meteorological, ecological, and agricultural applications, while the coarse resolution limits its utilization in regional scale. The regression tree-based machine learning algorithms reveal promising capability in SM downscaling. However, it lacks systematic study dedicated to intercomparisons of algorithms to explicitly illuminate their characteristics. In this study, comparisons are made to systematically evaluate performances of classification and regression tree (CART), random forest (RF), gradient boost decision tree (GBDT), and extreme gradient boost (XGB) in Soil Moisture Active Passive (SMAP) SM downscaling in southwest France. The results show that the four algorithms downscaled SM are capable of capturing spatial distribution features of the original SMAP SM. The downscaled regions with favorable accuracy are mostly situated in the dominant Mediterranean climate zone with moderate vegetation coverage and mild topography variation. The best results are obtained by GBDT in grassland with R value of 0.77 and ubRMSE value of 0.04 m 3 /m 3. The RF and XGB also achieve good performances. On the whole, the GBDT approach is robust and reliable, which could downscale SM with superior correlation and smaller bias than the others. Besides, it achieves higher accuracy than the original SMAP in grassland and shrubland. The feature importance index of each explainable variable fluctuates regularly among different seasons and models. This study proves the outstanding performance of GBDT in SMAP SM downscaling and is expected to act as a valuable reference for studies focusing on SM scale conversion algorithms.

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Section: Methodsmentioning

confidence: 99%

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

Liu

Xia

Yao

et al. 2020

Earth and Space Science

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“…Investigating the classifciation ML model accuracy, e.g., the logistic regression (LR), based on recursively removed features and those that remain, recursive feature elimination (RFE) has been used to reliably identify which features contribute the most to predicting the target property. This was corroborated using an alternative method, the random forest (RF) algorithm consists of a “forest” of decision trees (DTs), − each node of which splits the dataset based on a condition of a single feature so that similar properties are grouped together. Gini impurity was then used as measurement of how “similar” samples are, and this was calculated for each feature and tree, before averaging across the “forest”.…”

Section: Electronic Structure and Data Analytic Methodsmentioning

confidence: 99%

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction

Sun

Fernández

Barnard

2017

J. Chem. Inf. Model.

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Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.

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“…Many classifiers have been proposed to solve it. Decision tree [26] is one of the most widely used machine learning technologies. It represents a tree-like decision procedure for determining the class of a given instance.…”

Section: Fundamental Knowledge On Bagged Treementioning

confidence: 99%

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Peng

et al. 2020

Electronics

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High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

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Untitled

Cited by 265 publications

References 20 publications

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

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