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Asanao Shimokawa, Yohei Kawasaki, Etsuo Miyaoka
2014 Journal of the Japanese Society of Computational Statistics  
To construct a regression tree based on interval-valued symbolic variables, several models are considered.  ...  We address these problems and present an application of this model in reference to the study of HIV-1-infected patients' data.  ...  Acknowledgements The authors grateful to the referees and the editor for their careful reading of the manuscript and for helpful advices.  ... 
doi:10.5183/jjscs.1405001_211 fatcat:ngq2lteizbdxhnkv6xepkpfnou

Top-Down Induction of Decision Trees Classifiers—A Survey

L. Rokach, O. Maimon
2005 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
Decision trees are considered to be one of the most popular approaches for representing classifiers.  ...  This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner.  ...  Kolmogorov-Smirnov Criteria Friedman [18] and Rounds [19] have suggested a binary criterion that uses Kolmogorov-Smirnov distance.  ... 
doi:10.1109/tsmcc.2004.843247 fatcat:lzxedtztxfh3bj6gxfmuunxcwa

A Comparison of Decision Tree Induction with Binary Logistic Regression for the Prediction of the Risk of Cardiovascular Diseases in Adult Men

2018 Informatica  
The main purpose of this article was to compare traditional binary logistic regression analysis with decision tree analysis for the evaluation of the risk of cardiovascular diseases in adult men living  ...  for cardiovascular disease (CVD), but for consumers, the decision tree is easier to understand and interpret the results.  ...  results of the decision tree are easier to understand and to interpret.  ... 
doi:10.15388/informatica.2018.187 fatcat:smd3hxomwrcbbmnggodb2jvw4a

Tree-structured nonlinear signal modeling and prediction

O.J.J. Michel, A.O. Hero, A.E. Badel
1999 IEEE Transactions on Signal Processing  
In this paper, we develop a regression tree approach to identification and prediction of signals that evolve according to an unknown nonlinear state space model.  ...  We illustrate the method for two cases where classical linear prediction is ineffective: a chaotic "doublescroll" signal measured at the output of a Chua-type electronic circuit and a second-order ART  ...  Many discriminants are available for testing uniformity, including Kolmogorov-Smirnov tests [37] , rank-order statistical tests [16] , and scatter matrix tests [26] .  ... 
doi:10.1109/78.796437 fatcat:6hguc7whmjcbvlohywy2577kqa

Tree Based Method for Aggregate Survival Data Modeling

Asanao Shimokawa, Yoshitaka Narita, Soichiro Shibui, Etsuo Miyaoka
2016 The International Journal of Biostatistics  
by the expectation value of the number of individual descriptions of concepts.  ...  As a result, we obtained a new interpretation of the data in comparison to the classical survival tree modeling methods.  ...  In their research, the response variable was assumed to be a classical categorical variable, and the Kolmogorov-Smirnov criterion was used for the splitting rule. As introduced earlier, Quantin et al.  ... 
doi:10.1515/ijb-2015-0071 pmid:26882561 fatcat:3kja3nzddnbe5lxn3jetpno2mm

A Recursive Partitioning Decision Rule for Nonparametric Classification

1977 IEEE transactions on computers  
The criterion is both conceptually and computationally simple, and can be shown to have strong statistical merit. The resulting decision rule is asymptotically Bayes risk efficient.  ...  A new criterion for driving a recursive partitioning decision rule for nonparametric classification is presented.  ...  Kolmogorov-Smirnov criterion.  ... 
doi:10.1109/tc.1977.1674849 fatcat:laobrorkcngpboa7rfbcqsjp5q

ImbTreeEntropy and ImbTreeAUC: Novel R Packages for Decision Tree Learning on the Imbalanced Datasets

Krzysztof Gajowniczek, Tomasz Ząbkowski
2021 Electronics  
The packages accept all types of attributes, including continuous, ordered and nominal, where the latter type is simplified for multiclass problems to reduce the computational overheads.  ...  Both packages are applicable for binary and multiclass problems and they support cost-sensitive learning, by defining a misclassification cost matrix, and weighted-sensitive learning.  ...  The orthogonal (ORT) criterion was presented by [30] . A binary criterion that uses Kolmogorov-Smirnov distance has been proposed in [31] .  ... 
doi:10.3390/electronics10060657 fatcat:ap3jr3lhlvhqncillq2o26o33m

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Daniel Alves de Brito Filho, Rinaldo Artes
2018 Production  
Application of bayesian additive regression trees in the development of credit scoring models in Brazil, Production, 28, e20170110, https://doi.  ...  Main findings: The analysis confirms the superiority of the BART model over the LRM for the analyzed data. RF was superior to LRM only for the balanced sample.  ...  Another possibility of further investigation is the improvement of the models performance by using simultaneously credit bureau variables and the variables regularly used in application or behavior credit  ... 
doi:10.1590/0103-6513.20170110 fatcat:mfoax6t4h5cjfpp63kop233yna

A Chi-MIC Based Adaptive Multi-branch Decision Tree

Jiahao Ye, Jingjing Yang, Jiang Yu, Siqiao Tan, Feng Luo, Zheming Yuan, Yuan Chen
2021 IEEE Access  
Most of them tend to construct binary decision trees by some splitting criteria [10] , [11] , such as information gain, gain ratio, Gini value [12] , Kolmogorov-Smirnov distance [13] and histogram-based  ...  The minimization problem for decision trees is known to be NP-hard [2] , the binarization of data can simplify the growing of trees.  ... 
doi:10.1109/access.2021.3077125 fatcat:gvxyc3gqknd2rfvbhqkjviiedq

Genomic data analysis in tree spaces [article]

Sakellarios Zairis, Hossein Khiabanian, Andrew J. Blumberg, Raul Rabadan
2016 arXiv   pre-print
We then present a series of four biologically motivated applications to the analysis of genomic data, spanning cancer and infectious disease.  ...  To address this issue, we introduce tree dimensionality reduction, a structured approach to reducing large phylogenetic trees to a distribution of smaller trees.  ...  Acknowledgments The authors gratefully acknowledge the constructive feedback of Gillian Grindstaff, Melissa McGuirl, and Daniel Rosenbloom.  ... 
arXiv:1607.07503v1 fatcat:3pbgdgnk6nhgte5uuhx3tifbju

New Simplified Diagnostic Decision Trees for the Detention of Metabolic Syndrome in the Elderly

Enrique Rodríguez-Guerrero, Manuel Romero-Saldaña, Azahara Fernández-Carbonell, Rafael Molina-Luque, Guillermo Molina-Recio
2020 International Journal of Environmental Research and Public Health  
For the new detection method, decision trees were employed using automatic detection by interaction through Chi-square. Results: The prevalence of the MetS was of 43.7%.  ...  The final decision trees uses WC and basal glucose (BG), whose cutoff values were: for men, WC ≥ 102.5 cm and BG > 98 mg/dL (sensitivity = 67.1%, specificity = 90.3%, positive predictive value = 85%, validity  ...  To contrast the goodness of fit to a normal distribution of the data from continuous or discrete quantitative variables, when n > 50, the Kolmogorov-Smirnov test corrected by Lilliefors was used, and the  ... 
doi:10.3390/ijerph17145191 pmid:32708383 fatcat:d24z2v7aojco7jglyyudurl34e

Symbolic and spatial data analysis: Mining complex data structures

Paula Brito, Monique Noirhomme-Fraiture, Paula Brito, Monique Noirhomme-Fraiture
2006 Intelligent Data Analysis  
With the advent of the "information age", we have witnessed to a dramatic growth of applications in government, business and education, many of which are sources of various data, organised in different  ...  Object oriented databases, and, more recently, object-relational databases allow for the manipulation of data with complex structures, which then require novel methodologies of analysis.  ...  Acknowledgements We gratefully acknowledge the remarkable work of the members of the program committee of the workshop.  ... 
doi:10.3233/ida-2006-10401 fatcat:3riethdrknhednuasj5nsmxmmq

Splitting matters: how monotone transformation of predictor variables may improve the predictions of decision tree models [article]

Tal Galili, Isaac Meilijson
2016 arXiv   pre-print
It is widely believed that the prediction accuracy of decision tree models is invariant under any strictly monotone transformation of the individual predictor variables.  ...  However, this statement may be false when predicting new observations with values that were not seen in the training-set and are close to the location of the split point of a tree rule.  ...  While this study focused on a single split for a binary deterministic response variable (Y ), the results are in fact indicative to any type of recursive binary decision tree, be it a multiclass problem  ... 
arXiv:1611.04561v1 fatcat:pehyxvda6ncgpkz7qsbnyqnfvm

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

João Maroco, Dina Silva, Ana Rodrigues, Manuela Guerreiro, Isabel Santana, Alexandre de Mendonça
2011 BMC Research Notes  
The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5.  ...  Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia.  ...  None of the predictors showed a normal distribution judging from Kolmogorov-Smirnov with Lilliefors correction tests (p < 0.05), but criterion group variances were homogenous according to the Levene's  ... 
doi:10.1186/1756-0500-4-299 pmid:21849043 pmcid:PMC3180705 fatcat:lumtrdenjzhxraw4qe7jmwo27m

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Luay Fraiwan, Omnia Hassanin, Thippa Reddy Gadekallu
2021 PLoS ONE  
We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of  ...  This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs.  ...  In order to highlight the significance of ensembled predictions, we also considered the performance of the base decision tree model.  ... 
doi:10.1371/journal.pone.0252380 pmid:34086723 fatcat:yw5b324sqrd4dptgo6xruzigba
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