Results 31 to 40 of about 222,810 (285)
Severely imbalanced Big Data challenges: investigating data sampling approaches
Journal of Big Data, 2019 Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class.
Tawfiq Hasanin +3 more
doaj +1 more source
Imbalanced Learning Based on Data-Partition and SMOTE
Information, 2018 Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method
Huaping Guo, Jun Zhou, Chang-An Wu
doaj +1 more source
Semantic concept detection in imbalanced datasets based on different under-sampling strategies [PDF]
, 2011 Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes.
Foley, Colum +3 more
core +1 more source
Extending Bagging for Imbalanced Data [PDF]
, 2013 Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside ...
Jerzy Blaszczynski +2 more
openaire +1 more source
Oversampling Algorithm Oriented to Subdivision of Minority Class in Imbalanced Data Set [PDF]
Jisuanji gongcheng, 2017 The distributions of the minority class samples in the imbalanced data set are discrepant.Traditional oversampling algorithms do not dispose this discrepancy.In order to handle this discrepancy,this paper proposes an oversampling algorithm oriented to ...
GU Ping,YANG Yang
doaj +1 more source
SMOTE-LOF for noise identification in imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset.
Asniar +2 more
doaj +1 more source
Do unbalanced data have a negative effect on LDA? [PDF]
, 2008 For two-class discrimination, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562] claimed that, when covariance matrices of the two classes were unequal, a (class ...
Anderson +12 more
core +1 more source
Imbalanced Ensemble Classifier for learning from imbalanced business school data set
, 2018 Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature.
Chakraborty, Tanujit
core +1 more source
A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
Abstract and Applied Analysis, 2013 In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
doaj +1 more source
Multi-class Boosting for Imbalanced Data [PDF]
, 2015 We consider the problem of multi-class classification with imbalanced data-sets. To this end, we introduce a cost-sensitive multi-class Boosting algorithm (BAdaCost) based on a generalization of the Boosting margin, termed multi-class cost-sensitive margin.
Fernández Baldera, Antonio +2 more
openaire +2 more sources