KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling.
Hao DingBin WeiZhaorui GuZhibin YuHaiyong ZhengBing ZhengJuan LiPublished in: Multim. Tools Appl. (2020)
Keyphrases
- image classification
- imbalanced data
- base classifiers
- classifier ensemble
- ensemble methods
- svm classifier
- minority class
- sampling methods
- ensemble learning
- bag of words
- imbalanced datasets
- feature extraction
- image representation
- random forest
- class distribution
- support vector machine
- decision trees
- visual words
- class imbalance
- ensemble classifier
- imbalanced class distribution
- predictive accuracy
- multi label
- image features
- linear regression
- feature selection
- training set
- data sets
- neural network
- individual classifiers
- learning algorithm
- multi class
- prediction accuracy
- sampling algorithm
- random sampling
- sparse representation
- classification models
- monte carlo
- classification error
- naive bayes
- multi layer
- binary classification
- markov chain monte carlo
- class specific
- training data
- binary classification problems
- generalization ability
- sample size