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Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) Open
Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. Howeve…
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Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms Open
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing land…
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Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques Open
Diabetes causes a large number of deaths each year and a large number of people living with the disease do not realize their health condition early enough. In this study, we propose a data mining based model for early diagnosis and predict…
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GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models Open
The main purpose of this paper is to explore some potential applications of sophisticated machine learning techniques such as the kernel logistic regression, Naïve-Bayes tree and alternating decision tree models for landslide susceptibilit…
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A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China Open
The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers la…
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Logistic regression model training based on the approximate homomorphic encryption Open
We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.
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Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression Open
Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decisi…
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Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation Open
This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied…
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Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm Open
We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic …
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Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models Open
This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundw…
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Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms Open
Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease…
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Modeling Road Accident Severity with Comparisons of Logistic Regression, Decision Tree and Random Forest Open
To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparamet…
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Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble Open
Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In t…
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Classification models combined with Boruta feature selection for heart disease prediction Open
Cardiovascular disease (CVD), generally called heart illness, is a collective term for various ailments that affect the heart and blood vessels. Heart disease is a primary cause of fatality and morbidity in people worldwide, resulting in 1…
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Algorithms for maximum-likelihood logistic regression Open
Logistic regression is a workhorse of statistics and is closely related to methods used in Machine Learning, including the Perceptron and the Support Vector Machine. This note reviews seven different algorithms for finding the maximum-like…
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A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping Open
This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chi…
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Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms Open
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar …
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Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study Open
There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, lo…
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Inconsistency Between Univariate and Multiple Logistic Regressions. Open
Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression an…
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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran Open
This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along th…
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A comparison of penalised regression methods for informing the selection of predictive markers Open
Background Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional m…
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Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma Open
Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. In this paper, a novel diagnostics approach based on machine learning techniques is presented. Logis…
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Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping Open
Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has bee…
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Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization Open
Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In thi…
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Predicting Bankruptcy with Robust Logistic Regression Open
Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator vers…
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DecisionTree for Classification and Regression: A State-of-the Art Review Open
Classification and regression are defined under the umbrella of the prediction task of data mining. Discrete values are predicted using classification techniques whereas regression techniques are most suitable for predicting continuous dat…
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Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression Open
Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the …
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Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility Open
The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in th…
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Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China) Open
This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, str…
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Bootstrap internal validation command for predictive logistic regression models Open
Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predic…