scholarly journals ENTMOOT: A Framework for Optimization over Ensemble Tree Models

2021 ◽  
pp. 107343
Author(s):  
Alexander Thebelt ◽  
Jan Kronqvist ◽  
Miten Mistry ◽  
Robert M. Lee ◽  
Nathan Sudermann-Merx ◽  
...  
Keyword(s):  
Tree Models

A Hierarchy of Multinomial Models for Multidimensional Source Monitoring

Methodology ◽  
2005 ◽  
Vol 1 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Source Monitoring ◽  
Source Information ◽  
Model Comparisons ◽  
Storage And Retrieval ◽  
Multinomial Processing Tree ◽  
Multinomial Models ◽  
Hierarchical Relationship ◽  
Statistical Framework ◽  
Tree Models

Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information ( Meiser & Bröder, 2002 ), for partial source memory ( Dodson, Holland, & Shimamura, 1998 ) and for several sources ( Batchelder, Hu, & Riefer, 1994 ). The hierarchical relationship allows model comparisons and facilitates the specification of identifiability conditions. Conditions for global identifiability are discussed, and model comparisons are illustrated by reanalyses and by a new experiment on the storage and retrieval of multidimensional source information.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

2021 ◽  
pp. 0044118X2110046
Author(s):  
Veronica Fruiht ◽  
Jordan Boeder ◽  
Thomas Chan
Keyword(s):  
Critical Race Theory ◽  
Parental Education ◽  
Individual Characteristics ◽  
Conditional Inference ◽  
Youth Of Color ◽  
Social Success ◽  
Conditional Inference Tree ◽  
Tree Models ◽  
Nationally Representative ◽  
Inference Tree

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

scholarly journals CPT to RVU conversion improves model performance in the prediction of surgical case length

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas Garside ◽  
Hamed Zaribafzadeh ◽  
Ricardo Henao ◽  
Royce Chung ◽  
Daniel Buckland
Keyword(s):  
Model Performance ◽  
Individual Case ◽  
Model Complexity ◽  
Actual Case ◽  
Surgical Case ◽  
Relative Value ◽  
Prediction Time ◽  
Boosted Decision Tree ◽  
Tree Models ◽  
Duke University

AbstractMethods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT–RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model’s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.

scholarly journals Significance of pepsinogen in screening for gastric intestinal metaplasia in Guangdong, China

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052199049
Author(s):  
Xujuan Liu ◽  
Min Zhang ◽  
Riyu Luo ◽  
Keran Mo ◽  
Xingxiang He
Keyword(s):  
Logistic Regression ◽  
Intestinal Metaplasia ◽  
Single Factor ◽  
Antibody Testing ◽  
Cross Sectional ◽  
Gastric Intestinal Metaplasia ◽  
Risk Detection ◽  
Tree Models ◽  
Single Factor Analysis ◽  
Serological Biomarkers

Objective Diagnosis of gastric intestinal metaplasia (GIM) relies on gastroscopy and histopathologic biopsy, but their application in screening for GIM is limited. We aimed to identify serological biomarkers of GIM via screening in Guangdong, China. Methods Cross-sectional field and questionnaire data, demographic information, past medical history, and other relevant data were collected. Blood samples were collected for pepsinogen (PG)I, PGII, gastrin-17, and Helicobacter pylori antibody testing, and gastroscopy and histopathologic biopsy were performed. Single factor and logistic regression analyses were used to evaluate the correlation between these indicators and GIM, and decision tree models were used to determine the cut-off points between indicators. Results Of 443 participants enrolled, 87 (19.6%) were diagnosed with GIM. Single factor analysis showed that pepsin indicators (PGI, PGII, and PGI/PGII ratio) and the factors Mandarin as native language, urban residency, hyperlipidemia, and age were associated with GIM. Logistic regression analysis showed that PGI and age were associated with GIM. Conclusions Age is an important factor for predicting GIM progression; age >60 years increased its risk. Detection of GIM was higher in individuals with PGI levels >127.20 ng/mL, which could be used as a threshold indicating the need to perform gastroscopy and histopathologic biopsy.

scholarly journals Parking function varieties for combinatorial tree models

2021 ◽  
Vol 128 ◽  
pp. 102191
Author(s):  
Alois Panholzer
Keyword(s):  
Parking Function ◽  
Tree Models

scholarly journals Plastid phylogenomics resolves ambiguous relationships within the orchid family and provides a solid timeframe for biogeography and macroevolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria Alejandra Serna-Sánchez ◽  
Oscar A. Pérez-Escobar ◽  
Diego Bogarín ◽  
María Fernanda Torres-Jimenez ◽  
Astrid Catalina Alvarez-Yela ◽  
...  
Keyword(s):  
Phylogenetic Relationships ◽  
Divergence Times ◽  
Molecular Clocks ◽  
Phylogenetic Placement ◽  
Plastid Genomes ◽  
Phylogenetic Reconstructions ◽  
A Genome ◽  
Tree Models ◽  
Phylogenomic Analyses ◽  
First Time

AbstractRecent phylogenomic analyses based on the maternally inherited plastid organelle have enlightened evolutionary relationships between the subfamilies of Orchidaceae and most of the tribes. However, uncertainty remains within several subtribes and genera for which phylogenetic relationships have not ever been tested in a phylogenomic context. To address these knowledge-gaps, we here provide the most extensively sampled analysis of the orchid family to date, based on 78 plastid coding genes representing 264 species, 117 genera, 18 tribes and 28 subtribes. Divergence times are also provided as inferred from strict and relaxed molecular clocks and birth–death tree models. Our taxon sampling includes 51 newly sequenced plastid genomes produced by a genome skimming approach. We focus our sampling efforts on previously unplaced clades within tribes Cymbidieae and Epidendreae. Our results confirmed phylogenetic relationships in Orchidaceae as recovered in previous studies, most of which were recovered with maximum support (209 of the 262 tree branches). We provide for the first time a clear phylogenetic placement for Codonorchideae within subfamily Orchidoideae, and Podochilieae and Collabieae within subfamily Epidendroideae. We also identify relationships that have been persistently problematic across multiple studies, regardless of the different details of sampling and genomic datasets used for phylogenetic reconstructions. Our study provides an expanded, robust temporal phylogenomic framework of the Orchidaceae that paves the way for biogeographical and macroevolutionary studies.

scholarly journals Restoring steppe landscapes: patterns, drivers and implications in Russia’s steppes

2020 ◽  
Author(s):  
Robert Pazur ◽  
Alexander V. Prishchepov ◽  
Ksenya Myachina ◽  
Peter H. Verburg ◽  
Sergey Levykin ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Regression Tree ◽  
Agricultural Land Use ◽  
Virgin Land ◽  
Human Footprint ◽  
Steppe Belt ◽  
Tree Models ◽  
Southern Russia

Abstract Context Agricultural land abandonment across the steppe belt of Eurasia has provided an opportunity for the restoration of steppe landscapes in recent decades. However, global food demands are about to revert this trajectory and put restored steppe landscapes at risk. Objectives We analysed steppe development in southern Russia in the last 40 years, assessed its spatial patterns and drivers of change for several periods. Methods Using Landsat imagery, we mapped the permanent steppe and steppe restoration from 1990 to 2018. Based on regression tree models, we evaluate and explain its dynamics. Results were compared with district-level trends in land-use intensities of cropland. Results We found 70% of the steppe in 2018 represented permanent steppe and 30% of former cropland dominantly abandoned in the postsocialism (1990–2000). The permanent steppe and steppe restored in the postsocialism (1990–2000) were located far from settlements, on rough terrain and in districts of the Virgin Land Campaign (1954–1963). In recent decades, the patterns of steppe restoration (2000–2018) were mostly determined by unfavourable agroclimatic conditions and distance from grain storage facilities. The restoration pattern reflects regional differences in land-use intensities, e.g., isolated steppe patches mostly appeared in areas of intensive agricultural land-use. Conclusions Steppe restoration has appeared in areas marginal for agricultural production, with poor natural conditions and little human footprint. Consequently, the permanent steppe became less fragmented and a more continuous steppe landscape resulted. The remaining isolated steppe patches require attention in restoration programs as they are mostly located in areas of intensive agricultural land-use.

A Practical Tutorial for Decision Tree Induction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-38
Author(s):  
Víctor Adrián Sosa Hernández ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez ◽  
Octavio Loyola-González ◽  
Francisco Herrera
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Machine Learning Techniques ◽  
Evaluation Measures ◽  
Decision Tree Induction ◽  
Learning Techniques ◽  
Tree Models ◽  
Evaluation Measure ◽  
Main Components ◽  
Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

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