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A hybrid ensemble method for improved prediction of slope stability
Chongchong Qi
,
Xiaolin Tang
School of Engineering
UWA Business School
Research output
:
Contribution to journal
›
Article
›
peer-review
55
Citations (Scopus)
Overview
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Dive into the research topics of 'A hybrid ensemble method for improved prediction of slope stability'. Together they form a unique fingerprint.
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Computer Science
Ensemble Method
100%
Artificial Intelligence
100%
Individual Classifier
66%
Genetic Algorithm
66%
classifier ensemble
66%
ensemble classifier
66%
Characteristic Curve
66%
Validation Method
33%
Performance Measure
33%
Decision Tree
33%
Combination Method
33%
True Positive Rate
33%
Searching Algorithm
33%
Majority Voting
33%
True Negative
33%
Fold Cross Validation
33%
Discriminant Analysis
33%
Support Vector Machine
33%
Predictive Performance
33%
Adaptive Artificial Neural Network
33%
Process Classification
33%
Leaning Parameter
33%
Mathematics
Characteristic Curve
100%
Genetic Algorithm
100%
Nearest Neighbour
50%
Predictive Performance
50%
Robust Algorithm
50%
Cross-Validation
50%
Decision Tree
50%
Gaussian Process
50%
Neural Network
50%
Performance Measure
50%
Discriminant Analysis
50%
Search Algorithm
50%
Support Vector Machine
50%
Accurate Prediction
50%
Testing Set
50%
Tuning Weight
50%
Grid Search
50%
Positive Rate
50%
Engineering
Slope Stability
100%
Receiver Operating Characteristic
33%
Operating-Characteristic Curve
33%
Artificial Intelligence Technique
33%
Genetic Algorithm
33%
Accurate Prediction
16%
Performance Measure ψ
16%
Testing Set
16%
Gaussians
16%
Geomechanics
16%
Nearest Neighbor
16%
Artificial Neural Network
16%
Artificial Intelligence
16%
Support Vector Machine
16%
Physics
Slope Stability
100%
Artificial Intelligence
50%
Genetic Algorithm
33%
Neural Network
16%
Geomechanics
16%
Gaussian Distribution
16%
Chemical Engineering
Artificial Intelligence
100%
Neural Network
33%
Support Vector Machine
33%