SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Explore this paper's citation graph
Summary
This paper presents a novel approach for learning from imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure, which shows improvement in prediction performance on the minority class and overall improved F-values.
- Published
- 2003-09-22
- Cited by
- 1,790
- References
- 32
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2391953
References
- Data Mining for Direct Marketing: Problems and Solutions
- Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection
- PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection)
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Detecting Novel Network Intrusions Using Bayes Estimators
- Programs for Machine Learning
- The Case against Accuracy Estimation for Comparing Induction Algorithms
- A false friends exercise with authentic material retrieved from a corpus
- AdaCost: Misclassification Cost-Sensitive Boosting
- Data Mining Approaches for Intrusion Detection
- Machine Learning for the Detection of Oil Spills in Satellite Radar Images
- Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation
- Predicting rare classes: can boosting make any weak learner strong?
- A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
- Toward memory-based reasoning
- The Relationship between Recall and Precision
- COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY
- UCI Repository of machine learning databases
- Robust Classification for Imprecise Environments
- A Comparative Study of Cost-Sensitive Boosting Algorithms
Cited by
- Software defect prediction using static code metrics : formulating a methodology
- Transfer Learning for Class Imbalance Problems with Inadequate Data
- Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition
- Embedding Undersampling Rotation Forest for Imbalanced Problem
- Subspace-based Semantic Concept Detection and Retrieval for Multimedia Information Systems
- Wastewater's total influent estimation and performance modeling: a data driven approach
- Data mining with imbalanced class distributions: concepts and methods
- Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction
- On multi-class classification through the minimization of the confusion matrix norm
- Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing
- Ensemble diversity for class imbalance learning
- Handling imbalanced datasets: A review
- Algoritmos para la clasificación multiinstancia
- Anomaly detection for internet banking using supervised learning on high dimensional data
- Geometric Mean based Boosting Algorithm to Resolve Data Imbalance Problem
- Detection of Novel Network Attacks Using Data Mining
- Detection and Summarization of Novel Network Attacks Using Data Mining
- Incremental learning of concept drift from imbalanced data
- An Adaptive Sampling Ensemble Classifier for Learning from Imbalanced Data Sets
- Classification and multivariate analysis for complex data structures
Related papers
No related papers recorded.