Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

Geomorphology ◽  
2014 ◽  
Vol 204 ◽  
pp. 287-301 ◽  
Author(s):  
Ling Peng ◽  
Ruiqing Niu ◽  
Bo Huang ◽  
Xueling Wu ◽  
Yannan Zhao ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Landslide Susceptibility ◽  
Rough Set Theory ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Three Gorges ◽  
The Three Gorges ◽  
Vector Machines

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China

2019 ◽  
Vol 9 (22) ◽  
pp. 4756 ◽  
Author(s):  
Lanbing Yu ◽  
Ying Cao ◽  
Chao Zhou ◽  
Yang Wang ◽  
Zhitao Huo
Keyword(s):  
Landslide Susceptibility ◽  
Information Gain ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
The Three Gorges ◽  
Vector Machines ◽  
Information Gain Ratio

Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA.

scholarly journals Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
N. Pérez-Díaz ◽  
D. Ruano-Ordás ◽  
F. Fdez-Riverola ◽  
J. R. Méndez
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Wide Range ◽  
Vector Machines ◽  
Bayes Algorithm

Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

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