A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam

2014 ◽  
pp. 87-111 ◽  
Author(s):  
Dieu Tien Bui ◽  
Biswajeet Pradhan ◽  
Inge Revhaug ◽  
Chuyen Trung Tran
Keyword(s):  
Decision Tree ◽  
Spatial Prediction ◽  
Shallow Landslides ◽  
Comparative Assessment ◽  
Tree Models ◽  
J48 Decision Tree

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.

scholarly journals Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2849
Author(s):  
Sungbum Jun
Keyword(s):  
Decision Tree ◽  
Evolutionary Algorithm ◽  
Decision Trees ◽  
Manufacturing Systems ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Industrial Internet ◽  
Tree Models ◽  
Real World Datasets

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

Cost and Hazard Decision Tree Models

2017 ◽  
pp. 23-31
Author(s):  
Hamed Fazlollahtabar ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Decision Tree ◽  
Tree Models

scholarly journals Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3092 ◽  
Author(s):  
Shih-Hsiung Liang ◽  
Bruno Andreas Walther ◽  
Bao-Sen Shieh
Keyword(s):  
Decision Tree ◽  
Species Traits ◽  
Gradient Boosting ◽  
Small Data ◽  
Invasion Process ◽  
Data Set ◽  
Establishment Success ◽  
Exotic Birds ◽  
Tree Models ◽  
Nominal Variables

Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Discussion Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

2022 ◽  
Author(s):  
Seung-Bo Lee ◽  
Yong-Jeong Kim ◽  
Sungeun Hwang ◽  
Hyoshin Son ◽  
Sang Kun Lee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Gradient Boosting ◽  
Tree Models

Judging Mental Health Disorders Using Decision Tree Models

2021 ◽  
Author(s):  
Hoan Luong Cu Si
Keyword(s):  
Mental Health ◽  
Decision Tree ◽  
Psychological Health ◽  
Health Hazard ◽  
Mental Health Disorders ◽  
Medical Professionals ◽  
Tree Models ◽  
Health Disorders ◽  
Medical Analysis

This research presents a categorization replica to have the discernment of the result of distinct psychological health hazard which got improved with the implementation of the replica of decision tree. Among 3000 contestants approximately for different medical analysis, we get the instruction data regarding decision tree information from the answers of the queries. It is displayed by the exploratory outcomes that the suggested replica of the decision tree can find the significant framing of conclusion which influences Clinical discernment Precision. Such conclusions framing comprising in result such as recurrence or non-recurrence for clinical physical sickness, maturity, sex, duration of psychologically physical sickness, span for having drugs as well as suggested drugs that will be able to be applied as an instance of the assessment of the comprehensive precision of medical professionals.

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