Cost-sensitive boosting for classification of imbalanced data

From MaRDI portal
Publication:996413

DOI10.1016/j.patcog.2007.04.009zbMath1122.68505OpenAlexW2103614420MaRDI QIDQ996413

Mohamed S. Kamel, Yanmin Sun, Yang Wang, Andrew K. C. Wong

Publication date: 14 September 2007

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.04.009




Related Items (41)

Angle-based cost-sensitive multicategory classificationCost-sensitive ensemble learning: a unifying frameworkCost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIAImbalanced data classification using second-order cone programming support vector machinesMulti-class boosting with asymmetric binary weak-learnersGrouped Variable Selection Using Area under the ROC with Imbalanced DataCost-sensitive boosting algorithms: do we really need them?FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRALProgressive random \(k\)-labelsets for cost-sensitive multi-label classificationA cost-sensitive ensemble method for class-imbalanced datasetsCost Sensitive SVM with Non-informative Examples Elimination for Imbalanced Postoperative Risk Management ProblemApplication of credit‐scoring methods in a decision support system of investment for peer‐to‐peer lendingHigh dimensional binary classification under label shift: phase transition and regularizationThe improved AdaBoost algorithms for imbalanced data classificationAFNFS: adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced dataIntegrated Fisher linear discriminants: an empirical studyA review of boosting methods for imbalanced data classificationTraining and assessing classification rules with imbalanced dataCalibrated asymmetric surrogate lossesUnnamed ItemA noise-detection based AdaBoost algorithm for mislabeled dataLearning SVM with weighted maximum margin criterion for classification of imbalanced dataEnhancing techniques for learning decision trees from imbalanced dataBounding the difference between RankRC and RankSVM and application to multi-level rare class kernel rankingImbalanced classification in sparse and large behaviour datasetsFEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETSHierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-setsEvaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiersAn asymmetric classifier based on partial least squaresLarge margin cost-sensitive learning of conditional random fieldsDelta Boosting Machine with Application to General InsuranceCost-sensitive boosting for classification of imbalanced dataHandling imbalance in hierarchical classification problems using local classifiers approachesAn overlap sensitive neural network for class imbalanced dataA closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learningRegularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterionAn Improved Algorithm for SVMs Classification of Imbalanced Data SetsOR Practice–Data Analytics for Optimal Detection of Metastatic Prostate CancerConstrained Naïve Bayes with application to unbalanced data classificationCOST-SENSITIVE MULTI-CLASS ADABOOST FOR UNDERSTANDING DRIVING BEHAVIOR BASED ON TELEMATICSLogistic discrimination based on G-mean and F-measure for imbalanced problem


Uses Software


Cites Work


This page was built for publication: Cost-sensitive boosting for classification of imbalanced data