Handling binary classification problems with a priority class by using Support Vector Machines
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Summary
Experiments show that the modified SVM satisfies the aims for which it has been designed and results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered.
- Type
- article
- Published
- 2017-12-01
- Cited by
- 18
- References
- 30
- OpenAlex
- https://openalex.org/W2747895175
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:37703777
Keywords
Support vector machine, Binary classification, Computer science, Structured support vector machine, Pattern recognition (psychology)
References
- The Foundations of Cost-Sensitive Learning
- A comparison of methods for multiclass support vector machines
- Support vector machines for classification of input vectors with different metrics
- Dual unification of bi-class support vector machine formulations
- Multi-Classification by Using Tri-Class SVM
- Rapid and brief communication: Unified dual for bi-class SVM approaches
- Using partial least squares and support vector machines for bankruptcy prediction
- Learning from imbalanced data in surveillance of nosocomial infection
- A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information
- GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
- Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients
- Comparative analysis of data mining methods for bankruptcy prediction
- UCI Repository of machine learning databases
- Data mining for improved cardiac care
- A Note on the Bias in SVMs for Multiclassification
- Learning from Imbalanced Data
- An expert system for detection of breast cancer based on association rules and neural network
- A data-driven approach to predict the success of bank telemarketing
- Detection of financial statement fraud and feature selection using data mining techniques
- Prediction of liquefaction potential based on CPT up-sampling
Cited by
- OLLAWV: OnLine Learning Algorithm using Worst-Violators
- The application of active learning in identification of students with financial difficulties
- Biased support vector machine and weighted-smote in handling class imbalance problem
- Temporally-aware algorithms for the classification of anuran sounds
- Class-specific kernelized extreme learning machine for binary class imbalance learning
- Enhanced Super-Resolution Mapping of Urban Floods Based on the Fusion of Support Vector Machine and General Regression Neural Network
- A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer
- Automatic Detection of Motorcycle on the Road using Digital Image Processing
- WEDA: A Weak Emission-Line Detection Algorithm Based on the Weighted Ranking
- Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem
- A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity
- Prediction of Financial Statement Fraud using Machine Learning Techniques in UAE
- Evolving data-adaptive support vector machines for binary classification
- Multicondition operation fault detection for chillers based on global density-weighted support vector data description
- On predicting school dropouts in Egypt: A machine learning approach
- Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
- Predicting Heart Disease Using Automated Machine Learning Based on Genetic Algorithms
- Literature Review on Big Data Analytics Methods
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