Results 61 to 70 of about 103,585 (306)
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
An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Silke Janitza +5 more
core +1 more source
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
Processing imbalanced medical data at the data level with assisted-reproduction data as an example
BioData MiningObjective Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification
Junliang Zhu +6 more
doaj +1 more source
Imbalanced data classification using MapReduce and relief
Journal of Information and Telecommunication, 2018 Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance.
Joanna Jedrzejowicz +3 more
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
Ensemble Approach for the Classification of Imbalanced Data [PDF]
, 2009 Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diversity between individual base-learners, an ensemble will not suffer from overfitting. On the other hand, in many cases we are dealing with imbalanced data and a classifier which was built using all data has tendency to ignore ...
Vladimir Nikulin +2 more
openaire +4 more sources
Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation
FEBS Letters, EarlyView.Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz +11 more
wiley +1 more source
Research and application of XGBoost in imbalanced data
International Journal of Distributed Sensor Networks, 2022 As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but its classification effect in the case of data imbalance is often not ideal.
Ping Zhang, Yiqiao Jia, Youlin Shang
doaj +1 more source
Learning from Imbalanced Data Distribution
, 2023 As a prominent component of artificial intelligence (AI), machine learning (ML) techniques play a significant role in the stunning achievement obtained by AI technologies in human society.
Wang, Wentao
core +1 more source