scholarly journals Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

2018 ◽  
Vol 10 (4) ◽  
pp. 580 ◽  
Author(s):  
Tedros Berhane ◽  
Charles Lane ◽  
Qiusheng Wu ◽  
Bradley Autrey ◽  
Oleg Anenkhonov ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Decision Tree ◽  
Wetland Mapping ◽  
Multispectral Imagery ◽  
Rule Based ◽  
Random Forest Classification ◽  
Forest Classification

Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

2014 ◽  
Vol 5 (2) ◽  
pp. 157-164 ◽  
Author(s):  
Rei Sonobe ◽  
Hiroshi Tani ◽  
Xiufeng Wang ◽  
Nobuyuki Kobayashi ◽  
Hideki Shimamura
Keyword(s):  
Random Forest ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Crop Type ◽  
Multi Temporal

scholarly journals Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers

2021 ◽  
Vol 10 ◽  
Author(s):  
Nicolas Aide ◽  
Nicolas Elie ◽  
Cécile Blanc-Fournier ◽  
Christelle Levy ◽  
Thibault Salomon ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Clinical Stage ◽  
Metastatic Breast ◽  
Breast Tumors ◽  
Breast Cancers ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Kaplan Meier ◽  
Metabolic Heterogeneity

IntroductionWe aimed to investigate whether 18F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS).Materials and methodsOn 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUVmax, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables.ResultsFor ER expression, correlations were mainly observed with 18F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness_ER and uniformity_HISTO (ρ = −0.386, p = 0.017) and correlation_PR and entropy_GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUVmax, skewness_ER, kurtosis_ER, entropy_HISTO, and uniformity_HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness_ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness_ER, none of the parameters were independent predictors. Indeed, skewness_ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023).ConclusionA heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

scholarly journals SYBA: Bayesian estimation of synthetic accessibility of organic compounds

2020 ◽  
Author(s):  
Milan Voršilák ◽  
Michal Kolář ◽  
Ivan Čmelo ◽  
Daniel Svozil
Keyword(s):  
Random Forest ◽  
Organic Compounds ◽  
General Public ◽  
Bayes Classifier ◽  
Baseline Method ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Synthetic Accessibility ◽  
General Public License

Abstract SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to ~4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

2012 ◽  
Vol 121 ◽  
pp. 93-107 ◽  
Author(s):  
V.F. Rodriguez-Galiano ◽  
M. Chica-Olmo ◽  
F. Abarca-Hernandez ◽  
P.M. Atkinson ◽  
C. Jeganathan
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Random Forest Classification ◽  
Forest Classification
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