Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

2015 ◽  
Vol 128 (1-2) ◽  
pp. 255-273 ◽  
Author(s):  
Binh Thai Pham ◽  
Dieu Tien Bui ◽  
Hamid Reza Pourghasemi ◽  
Prakash Indra ◽  
M. B. Dholakia
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Landslide Susceptibility ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Comparison Study ◽  
Prediction Capability

scholarly journals Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

2012 ◽  
Vol 2012 ◽  
pp. 1-26 ◽  
Author(s):  
Dieu Tien Bui ◽  
Biswajeet Pradhan ◽  
Owe Lofman ◽  
Inge Revhaug
Keyword(s):  
Support Vector Machines ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Landslide Inventory ◽  
Slope Aspect ◽  
Support Vector ◽  
Vector Machines ◽  
Prediction Capability

The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower.

scholarly journals Machine Learning Readmission Risk Modeling: A Pediatric Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Patricio Wolff ◽  
Manuel Graña ◽  
Sebastián A. Ríos ◽  
Maria Begoña Yarza
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Naive Bayes ◽  
Class Imbalance ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Distribution Model ◽  
Training Dataset ◽  
Support Vector ◽  
Pediatric Hospital

Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

2007 ◽  
Vol 237 (4) ◽  
pp. 377-385 ◽  
Author(s):  
Nima Vaziri ◽  
Alireza Hojabri ◽  
Ali Erfani ◽  
Mehrdad Monsefi ◽  
Behnam Nilforooshan
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Radial Basis Function ◽  
Critical Heat Flux ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Comparison Study ◽  
Radial Basis

scholarly journals Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

2020 ◽  
Vol 1641 ◽  
pp. 012068
Author(s):  
Diah Puspitasari ◽  
Kresna Ramanda ◽  
Adi Supriyatna ◽  
Mochamad Wahyudi ◽  
Erma Delima Sikumbang ◽  
...  
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preterm Birth ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithms ◽  
Artificial Neural ◽  
Mining Algorithms

Text Classification Using Self-Structure Extended Multinomial Naive Bayes

2020 ◽  
pp. 107-129
Author(s):  
Arun Solanki ◽  
Rajat Saxena
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Model ◽  
Text Classification ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
The Many ◽  
Feature Dependency ◽  
Direct Dependency

With the advent of neural networks and its subfields like deep neural networks and convolutional neural networks, it is possible to make text classification predictions with high accuracy. Among the many subtypes of naive Bayes, multinomial naive Bayes is used for text classification. Many attempts have been made to somehow develop an algorithm that uses the simplicity of multinomial naive Bayes and at the same time incorporates feature dependency. One such effort was put in structure extended multinomial naive Bayes, which uses one-dependence estimators to inculcate dependencies. Basically, one-dependence estimators take one of the attributes as features and all other attributes as its child. This chapter proposes self structure extended multinomial naïve Bayes, which presents a hybrid model, a combination of the multinomial naive Bayes and structure extended multinomial naive Bayes. Basically, it tries to classify the instances that were misclassified by structure extended multinomial naive Bayes as there was no direct dependency between attributes.

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