Using a classification tree to identify seepage in flood embankments

2022 ◽  
Vol 1 (1) ◽  
pp. 113-116
Author(s):  
Krzysztof KRÓL
Keyword(s):  
Classification Tree

Profiling older community-dwellers with hypovitaminosis D: A classification tree analysis

2020 ◽  
Vol 90 (3-4) ◽  
pp. 195-199 ◽  
Author(s):  
Gaelle Chevallereau ◽  
Mathilde Legeay ◽  
Guillaume T. Duval ◽  
Spyridon N. Karras ◽  
Bruno Fantino ◽  
...  
Keyword(s):  
Vitamin D ◽  
Classification Tree ◽  
Hypovitaminosis D ◽  
Vitamin D Supplementation ◽  
Community Dwelling ◽  
Gait Disorders ◽  
Hydroxyvitamin D ◽  
Chaid Analysis ◽  
Clinical Profiles ◽  
Community Dwellers

Abstract. Despite the high prevalence of hypovitaminosis D in older adults, universal vitamin D supplementation is not recommended due to potential risk of intoxication. Our aim here was to determine the clinical profiles of older community-dwellers with hypovitaminosis D. The perspective is to build novel strategies to screen for and supplement those with hypovitaminosis D. A classification tree (CHAID analysis) was performed on multiple datasets standardizedly collected from 1991 older French community-dwelling volunteers ≥ 65 years in 2009–2012. Hypovitaminosis D was defined as serum 25-hydroxyvitamin D ≤ 50 nmol/L. CHAID analysis retained 5 clinical profiles of older community-dwellers with different risks of hypovitaminosis D up to 87.3%, based on various combinations of the following characteristics: polymorbidity, obesity, sadness and gait disorders. For instance, the probability of hypovitaminosis D was 1.42-fold higher [95CI: 1.27–1.59] for those with polymorbidity and gait disorders compared to those with no polymorbidity, no obesity and no sadness. In conclusion, these easily-recordable measures may be used in clinical routine to identify older community-dwellers for whom vitamin D supplementation should be initiated.

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

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

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

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, F1-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, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Perceived vulnerability to COVID-19 infection from event attendance: results from Louisiana, USA, two weeks preceding the national emergency declaration

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ran Li ◽  
Bingcheng Yang ◽  
Jerrod Penn ◽  
Bailey Houghtaling ◽  
Juan Chen ◽  
...  
Keyword(s):  
Public Health ◽  
Food Preference ◽  
Classification Tree ◽  
Personal Characteristics ◽  
Initial Response ◽  
Income Group ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Early Stages ◽  
Preference Study

Abstract Background Individual perceptions of personal and national threats posed by COVID-19 shaped initial response to the pandemic. The aim of this study was to investigate the changes in residents’ awareness about COVID-19 and to characterize those who were more aware and responsive during the early stages of the pandemic in Louisiana. Methods In response to the mounting threat of COVID-19, we added questions to an ongoing food preference study held at Louisiana State University from March 3rd through March 12th, 2020. We asked how likely it was that the spread of the coronavirus will cause a national public health crisis and participants’ level of concern about contracting COVID-19 by attending campus events. We used regression and classification tree analysis to identify correlations between these responses and (a) national and local COVID case counts; (b) personal characteristics and (c) randomly assigned information treatments provided as part of the food preference study. Results We found participants expressed a higher likelihood of an impending national crisis as the number of national and local confirmed cases increased. However, concerns about contracting COVID-19 by attending campus events rose more slowly in response to the increasing national and local confirmed case count. By the end of this study on March 12th, 2020 although 89% of participants agreed that COVID-19 would likely cause a public health crisis, only 65% of the participants expressed concerns about contracting COVID-19 from event attendance. These participants were significantly more likely to be younger students, in the highest income group, and to have participated in the study by responding to same-day, in-person flyer distribution. Conclusions These results provide initial insights about the perceptions of the COVID-19 public health crisis during its early stages in Louisiana. We concluded with suggestions for universities and similar institutions as in-person activities resume in the absence of widespread vaccination.

scholarly journals Copula-Based Bayesian Reliability Analysis of a Product of a Probability and a Frequency Model for Parallel Systems When Components Are Dependent

2021 ◽  
Vol 11 (4) ◽  
pp. 1697
Author(s):  
Shi-Woei Lin ◽  
Tapiwa Blessing Matanhire ◽  
Yi-Ting Liu
Keyword(s):  
System Reliability ◽  
Classification Tree ◽  
Parallel Systems ◽  
Reliability Model ◽  
Key Factors ◽  
Frequency Model ◽  
Independent Components ◽  
Aggregate Analysis ◽  
Dependence Assumption ◽  
Disaggregate Analysis

While the dependence assumption among the components is naturally important in evaluating the reliability of a system, studies investigating the issues of aggregation errors in Bayesian reliability analyses have been focused mainly on systems with independent components. This study developed a copula-based Bayesian reliability model to formulate dependency between components of a parallel system and to estimate the failure rate of the system. In particular, we integrated Monte Carlo simulation and classification tree learning to identify key factors that affect the magnitude of errors in the estimation of posterior means of system reliability (for different Bayesian analysis approaches—aggregate analysis, disaggregate analysis, and simplified disaggregate analysis) to provide important guidelines for choosing the most appropriate approach for analyzing a model of products of a probability and a frequency for parallel systems with dependent components.

Use of Statistical Techniques to Synthesize Explicit Criteria Developed by an Expert Panel

2006 ◽  
Vol 45 (06) ◽  
pp. 622-630 ◽  
Author(s):  
J. M. Quintana ◽  
A. Urkaregi ◽  
I. Arostegui
Keyword(s):  
Clinical Decision Making ◽  
Expert Panel ◽  
Multiple Correspondence Analysis ◽  
Classification Tree ◽  
Regression Tree ◽  
Clinical Decision ◽  
Statistical Techniques ◽  
Hip Joint Replacement ◽  
Weighted Probability ◽  
Explicit Criteria

Summary Objectives: Methodology based on expert panels has been commonly used to evaluate the appropriateness of interventions. An important issue is the adequate synthesis of the generated information in an applicable way to clinical decision making. This paper shows how statistical procedures help synthesize the results of an expert panel. Methods: Three statistical techniques were applied to an expert panel that developed explicit criteria to assess the appropriateness of total hip joint replacement: classification tree, regression tree and multiple correspondence analysis combined with automatic classification. Results: Results provided by the three models were shown in graphical displays and were compared to the original panel results using crude and weighted probability of misclassification. Results were also applied to real interventions in order to know the implication of the misclassification on real patients. Conclusions: The statistical techniques help summarize data from panels of experts and provide useful decision models for clinical practice, especially when the number of indications is big. However, degree of misclassification and its implication should be taken into account.

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