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Open AccessArticle

Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

by Omar Alaqeeli, Li Xing and Xuekui Zhang

Microbiol. Res. 2021, 12(2), 317-334; https://doi.org/10.3390/microbiolres12020022 - 7 Apr 2021

Cited by 2 | Viewed by 3191

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall,

F_{1}

-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall,

F_{1}

-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others. Full article

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22 pages, 7542 KB

Open AccessArticle

Soil Mapping Based on Globally Optimal Decision Trees and Digital Imitations of Traditional Approaches

by Arseniy Zhogolev and Igor Savin

ISPRS Int. J. Geo-Inf. 2020, 9(11), 664; https://doi.org/10.3390/ijgi9110664 - 4 Nov 2020

Cited by 5 | Viewed by 3458

Abstract

Most digital soil mapping (DSM) approaches aim at complete statistical model extraction. The value of the explicit rules of soil delineation formulated by soil-mapping experts is often underestimated. These rules can be used for expert testing of the notional consistency of soil maps, soil trend prediction, soil geography investigations, and other applications. We propose an approach that imitates traditional soil mapping by constructing compact globally optimal decision trees (EVTREE) for the covariates of traditionally used soil formation factor maps. We evaluated our approach by regional-scale soil mapping at a test site in the Belgorod region of Russia. The notional consistency and compactness of the decision trees created by EVTREE were found to be suitable for expert-based analysis and improvement. With a large sample set, the accuracy of the predictions was slightly lower for EVTREE (59%) than for CART (67%) and much lower than for Random Forest (87%). With smaller sample sets of 1785 and 1000 points, EVTREE produced comparable or more accurate predictions and much more accurate models of soil geography than CART or Random Forest. Full article

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