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Regional Patterns of Major Nonnative Invasive Plants and Associated Factors in Upper Midwest Forests

FOREST SCIENCE 2013年第1期59卷 38-49页

作者： Fan, Zhaofei Moser, W. Keith Hansen, Mark H. Nelson, Mark D. Mississippi State Univ Dept Forestry Mississippi State MS 39762 USA US Forest Serv USDA No Res Stn Morgantown WV USA

Nonnative invasive plants (IPs) are rapidly spreading into natural ecosystems (e.g., forests and grasslands). Potential threats of IP invasion into natural ecosystems include biodiversity loss, structural and environmental change, habitat degradation, and economic losses. The Upper Midwest of the United States encompasses the states of Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, and Wisconsin, a region populated with 46 million people. Concerns of IP threats to the productive timberlands in the region have emerged with rapid expansion of urban areas and associated land cover changes caused by increasing human disturbances. Using the strategic inventory data from the 2005-2006 US Department of Agriculture Forest Service Forest Inventory and Analysis program and other data such as forestland cover and transportation coverage/layers, we modeled the regional patterns of IPs by using a combination of nonparametric techniques, including classification and regression tree analysis, kernel density smoothing, and bootstrapping. For the Midwest region, a probability map and historical records of human-related introduction of IPs of interest suggests that invasive shrubs, herbs, and grasses were initially introduced into the central (sparsely forested) areas and then spread north and south (densely forested areas), whereas invasive vines spread primarily from the south into other parts of the region. The probability of IPs in densely forested areas (0.1) was one-fifth of that in sparsely forested areas. Shrubs are the predominant IP threat and are distributed across the vast region with the exception of the northern part. Invasive grasses and herbs are most abundant in the central part of the region, and invasive vines are most common in the southern part. Percent forest cover and road proximity (distance to roads) as indicators of anthropogenic disturbances, were the most significant drivers of IP occurrence/abundance. Site factors, including forest productivity a

关键词： nonnative invasive plant Forest Inventory Analysis kernel density smoothing probability classification and regression tree bootstrap

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Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2024年第10期250卷 2529-2544页

作者： Bhattacharyya, Suman Kumar Pal, Sagarika Narula Inst Technol 81 Nilgunj Rd Kolkata 700109 West Bengal India Natl Inst Tech Teachers Training & Res Block FCSect 3 Kolkata 700106 West Bengal India

Ensuring accurate classification of rice as either cooked or dry is vital for food safety, as improperly stored or cooked rice contain harmful bacteria, emphasizing the importance of maintaining and monitoring food safety standards. In the field of image analysis and food classification, classifying dry and cooked rice samples using photographs is an interesting but difficult task. The main challenge stems from the minute visual variations between cooked and dry rice, which has not always displayed distinct traits that are easily observable by machines. Hence, various machine learning algorithms were implemented to effectively mitigate this issue. However, the existing works have not analysed the physicochemical characteristics due to non-destructive type of experimentation method with image processing. To overcome this issue, this work develops the classification and regression tree (CART) of Decision tree Learning method for classifying the rice grain samples as dry or cooked based on the physicochemical characteristics such as morphological, texture and color features, which in turn gain an exhaustive facts of rice quality in diverse state. Initially, the images are captured, pre-processed and the features are extracted. From the extracted features, the rice samples are classified as dry and cooked using DT and the results are compared with the existing algorithms like K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). The comparative analysis of these classification algorithms infers the outperformance of the DT learning model under morphological, texture and color features in terms of accuracy, error, precision, recall and F-score.

关键词： Rice Decision tree Morphology Feature Texture Feature Color Feature classification and regression tree

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A hybrid FAM-CART model and its application to medical data classification

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NEURAL COMPUTING & APPLICATIONS 2015年第8期26卷 1799-1811页

作者： Seera, Manjeevan Lim, Chee Peng Tan, Shing Chiang Loo, Chu Kiong Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia Multimedia Univ Fac Informat Sci & Technol Melaka Malaysia

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability-plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM-CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM-CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability-plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM-CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM-CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

关键词： Fuzzy ARTMAP classification and regression tree Pattern classification Stability-plasticity dilemma Rule extraction Decision support

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Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

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ECOLOGICAL APPLICATIONS 2011年第6期21卷 2269-2282页

作者： Jones-Farrand, D. Todd Fearer, Todd M. Thogmartin, Wayne E. Thompson, Frank R., III Nelson, Mark D. Tirpak, John M. Univ Missouri Columbia MO 65211 USA Univ Arkansas Arkansas Forest Resources Ctr Sch Forest Resources Monticello AR 71655 USA USGS Upper Midwest Environm Sci Ctr La Crosse WI 54603 USA Forest Serv USDA No Res Stn Columbia MO 65211 USA Forest Serv USDA No Res Stn St Paul MN 55108 USA US Fish & Wildlife Serv Vicksburg MS 39180 USA

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant (P < 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservatio

关键词： Bayesian hierarchical model Breeding Bird Survey classification and regression tree forest inventory and analysis habitat suitability index model uncertainty species distribution model

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Bagging-based machine learning algorithms for landslide susceptibility modeling

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NATURAL HAZARDS 2022年第2期110卷 823-846页

作者： Zhang, Tingyu Fu, Quan Wang, Hao Liu, Fangfang Wang, Huanyuan Han, Ling Minist Nat Resources Key Lab Degraded & Unused Land Consolidat Engn Xian Shaanxi Peoples R China Shaanxi Prov Land Engn Construct Grp Co Ltd Inst Land Engn & Technol Xian Shaanxi Peoples R China Shaanxi Prov Land Engn Construct Grp Land Survey Xian Shaanxi Peoples R China Shaanxi Land Engn Construct Grp Co Ltd Hanzhong Branch Han Zhong Shaanxi Peoples R China Changan Univ Sch Land Engn Xian Shaanxi Peoples R China

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFtree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874;however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

关键词： Landslide susceptibility Bagging Best-first decision tree Functional tree classification and regression tree Support vector machine

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Data mining: Potential applications in research on nutrition and health

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NUTRITION & DIETETICS 2017年第1期74卷 3-10页

作者： Batterham, Marijka Neale, Elizabeth Martin, Allison Tapsell, Linda Univ Wollongong Natl Inst Appl Stat Res Australia Stat Consulting Ctr Wollongong NSW Australia Univ Wollongong Fac Sci Med & Hlth Sch Med Wollongong NSW Australia

Aim: Data mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention. Methods: Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. Results: The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables' importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P = 0.01). Conclusions: Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research.

关键词： artificial neural network classification and regression tree data mining support vector machine

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Monitoring and verifying agricultural practices related to soil carbon sequestration with satellite imagery

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AGRICULTURE ECOSYSTEMS & ENVIRONMENT 2007年第1-4期118卷 201-210页

作者： Bricklemyer, Ross S. Lawrence, Rick L. Miller, Perry R. Battogtokh, Norov Montana State Univ Dept Land Resources & Environm Sci Bozeman MT 59717 USA Mongolian Soc Sustainable Dev Ulaanbaatar 13 Mongolia

The Kyoto Protocol entering into force on 16 February 2005 continues to spur interest in development of carbon trading mechanisms internationally and domestically. Critical to the development of a carbon trading effort is verification that carbon has been sequestered, and field level measurement of C change is likely cost prohibitive. Estimating C change based on agricultural management practices related to carbon sequestration seems more realistic, and analysis of satellite imagery could be used to monitor and verify these practices over large areas. We examined using Landsat imagery to verify crop rotations and quantify crop residue biomass in north central Montana. Field data were collected using a survey of farms. Standard classification tree analysis (CTA) and boosted classification and regression tree analysis (BCTA) were used to classify crop types. Linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB) were used to estimate crop residue. Six crop types were classified with 97% accuracy (BCTA) with class accuracies of 88-99%. Paired t-tests were used to compare the difference between known and predicted mean crop residue biomass. The difference between known and predicted mean residues using SGB was not different than 0 (p-value = 0.99);however root mean square error (RMSE) was large (1981 kg ha(-1)), implying that SGB accurately predicted regional crop residue biomass but not local predictions (i.e., field or farm level). The results of this study, and previous research classifying tillage practices and estimating soil disturbance, supports using satellite imagery as an effective tool for monitoring and verifying agricultural management practices related to carbon sequestration over large areas. Published by Elsevier B.V.

关键词： carbon sequestration crop types crop residue biomass classification and regression tree boosting Landsat imagery

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Factors that affect visibility during endoscopic hemostasis for upper GI bleeding: a prospective study

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GASTROINTESTINAL ENDOSCOPY 2015年第6期81卷 1392-1400页

作者： Jung, Sungmo Kim, Eun Hye Kim, Ha Yan Roh, Yun Ho Park, Chan Hyuk Park, Soo Jung Chung, Hyunsoo Kim, Beom Kyung Lee, Hyuk Park, Jae Jun Hong, Sung Pil Park, Jun Yong Shin, Sung Kwan Lee, Sang Kil Lee, Yong Chan Park, Jun Chul Yonsei Univ Coll Med Dept Internal Med Severance HospInst Gastroenterol Seoul 120752 South Korea Yonsei Univ Coll Med Biostat Collaborat Unit Seoul 120752 South Korea Sungkyunkwan Univ Sch Med Samsung Med Ctr Div GastroenterolDept Med Seoul South Korea Yonsei Univ Coll Med Gangnam Severance Hosp Dept Internal MedInst Gastroenterol Seoul 120752 South Korea

Background: Adequate visibility is an important factor for achieving successful endoscopic hemostasis for the treatment of upper GI bleeding (UGIB). The independent factors that affect visibility during endoscopic procedures have yet to be determined. Objective: To determine the factors that affect endoscopic visibility and to create a model that can predict in which patients unacceptable visibility is suspected before emergent endoscopic procedures for UGIB. Design: Prospective, observational study. Setting: University-affiliated tertiary care hospital in South Korea. Patients: A total of 121 patients admitted because of UGIB. Intervention: Analysis of the visibility score of the emergency endoscopies for UGIB. Main Outcome Measurements: Factors affecting the visibility score of endoscopy and a classification and regression tree (CART) model for predicting of visibility. Results: The EGD time and the appearance of the nasogastric (NG) tube aspirate were independent factors that were significantly associated with visibility (EGD time, P < .001;red blood appearance in NG tube aspirate, P < .001;coffee grounds appearance of NG tube aspirate, P = .006). Based on these results, a CART model was developed by using 70 patients who had been allocated to the training set. The CART generated algorithms that proposed the use of the appearance of the NG tube aspirate and the EGD time (8.5 hours) to predict visibility. The sensitivity and specificity for predicting poor visibility were 71.4% and 86.4%, respectively. Conclusion: The use of the CART model enables the prediction of which patients will have poor visibility during emergent endoscopy.

关键词： AbbreviationsCART classification and regression tree NG nasogastric UGIB upper GI bleeding

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Correlation of radiographic measurements of structures of the equine foot with lesions detected on magnetic resonance imaging

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EQUINE VETERINARY JOURNAL 2016年第2期48卷 165-171页

作者： de Zani, D. Polidori, C. di Giancamillo, M. Zani, D. D. Univ Milan Azienda Polo Vet Lodi Reparto Radiol Lodi Italy

Reasons for performing studyThere are few studies on the correlations between radiographic measurements of the foot and abnormalities of specific structures found with magnetic resonance imaging (MRI). ObjectivesTo document the relationship between radiographic measurements of the equine foot and the presence of lesions in the foot on MRI. We hypothesised that different radiographic measurements would be associated with specific lesions detected by MRI. Study designRetrospective analysis of radiographs and MRI studies. MethodsSeventy-four feet from 52 lame horses were included. Twenty parameters were measured on radiographs, whereas the signal intensity, homogeneity and size of each structure in the foot were evaluated on magnetic resonance images. The data were analysed using simple linear correlation analysis and classification and regression trees (CARTs). ResultsLinear correlations were found between the navicular bone compacta thickness and injuries of the deep digital flexor tendon, collateral sesamoidean ligament, navicular spongiosa and navicular bone proximal border. Long-toed horses had a high incidence of lesions involving the spongiosa and proximal border of the navicular bone. Elongation of the navicular bone was associated with proximal and distal border injuries. A reduced palmar angle and increased angle between the middle and distal phalanx were observed in horses with alterations of collateral ligaments of the distal interphalangeal joint and navicular bone spongiosa, respectively. For each structure under investigation, CARTs predicting the presence of MRI pathology based on radiographic measurements had excellent performance, with >80% correct classification of cases, when using one of 3 data sources. ConclusionsThis study demonstrated a relationship between radiographic measurements of the foot and the presence of lesions detected on MRI, while CARTs illustrated that different radiographic measurements were associated with different MRI lesions.

关键词： horse magnetic resonance imaging radiography lameness measurements classification and regression tree

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Identifying the highest risk vascular patients: Insights from the XATOA registry

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AMERICAN HEART JOURNAL 2024年 269卷 191-200页

作者： Anand, Sonia S. Aboyans, Victor Bosch, Jackie Debus, Sebastian Gay, Alain Patel, Manesh R. Vogtlaender, Kai Welsh, Robert C. Zeymer, Uwe Fox, Keith A. A. Populat Hlth Res Inst Hamilton ON Canada Dupuytren Univ Hosp Limoges Univ Dept Cardiol Limoges France Limoges Univ Inserm 1094 IRD270 Limoges France McMaster Univ Sch Rehabil Sci Hamilton ON Canada Bayer AG Berlin Germany Duke Univ Durham NC USA Bayer AG Wuppertal Germany Mazankowski Alberta Heart Inst Edmonton AB Canada Univ Alberta Edmonton AB Canada Inst Herzinfarktforsch Ludwigshafen Ludwigshafen Germany Univ Edinburgh Ctr Cardiovasc Sci Edinburgh Scotland McMaster Univ Dept Med Hamilton ON Canada Univ Heart Ctr Hamburg Eppendorf Dept Cardiol Hamburg Germany Populat Hlth Res Inst 237 Barton St East Hamilton ON Canada

Background Patients with coronary and peripheral artery disease (PAD) have a residual risk of major adverse cardiovascular and limb events despite standards of care. Among patients with coronar y arter y disease (CAD) and/or PAD selected for low dose rivaroxaban (2.5 mg BID) and aspirin, we sought to determine the highest risk vascular patients. Methods Xarelto pluc Acetylsalicylic acid: Treatment patterns and Outcomes in patients with Atherosclerosis (XATOA) is a single-arm registry of CAD and/or PAD patients. All participants were initiated on low dose rivaroxaban (2.5 mg BID) and aspirin. We report the incidence risk of major adverse cardiovascular events (MACE) or major adverse limb events (MALE) and major bleeding. A classification and regression tree analysis determined independent subgroups. Results Between November 2018 and May 2020, 5,808 participants were enrolled in XATOA;5,532 were included in the full analysis. The median follow-up (interquartile range) was 462 (371-577) days. The incidence risk per 100 patientyears of MACE or MALE was highest among participants with polyvascular disease (2 or more vascular beds affected, n = 2,889). The incidence risk was 9.16 versus 2.48 per 100 patient-years in polyvascular and nonpolyvascular patients respectively. Other subgroups of high-risk patients included participants 75 years or older, with a history of diabetes, heart failure, or chronic renal insufficiency (CRI). Rates of major bleeding were low overall. A classification and regression tree analysis showed that polyvascular disease was the most dominant factor separating higher from lower risk participants, and this was heightened with CRI or diabetes. Conclusion Patients with polyvascular disease represent a substantial subset of patients in clinical practice and should be prioritized to receive maximal medical therapy including low dose rivaroxaban (2.5 mg BID) and aspirin. (Am Heart J 2024;269:191-200.)

关键词： ACE angiotensin converting enzyme ARB angiotensin receptor blockers CAD coronary artery disease CART classification and regression tree CI confidence intervals CRI chronic renal insufficiency DAPT Dual Anti-Platelet Treatment EUCLID Examining Use of Ticagrelor in Peripheral Arterial Disease ISTH International Society on Thrombosis and Haemostasis MACE major adverse cardiovascular events MALE major adverse limb events PAD peripheral artery disease TIA Transient Ischemic Attack XATOA Xarelto plus Acetylsalicylic acid: Treatment patterns and Outcomes in patients with Atherosclerosis

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