Results 41 to 50 of about 224,004 (328)
Pediatric Blood &Cancer, EarlyView.ABSTRACT Bone tumours present significant challenges for affected patients, as multimodal therapy often leads to prolonged physical limitations. This is particularly critical during childhood and adolescence, as it can negatively impact physiological development and psychosocial resilience.
Jennifer Queisser +5 more
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
Informatics in Medicine Unlocked, 2023 An imbalanced classification problem occurs when the distribution of samples among different classes is uneven or biased. Handling small and imbalanced training datasets poses a notable challenge in machine learning, especially in domains such as ...
Consolata Gakii +2 more
doaj +1 more source
Interpretable ML for Imbalanced Data
, 2022 Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear.
Dablain, Damien A. +4 more
openaire +2 more sources
Aggressive prostate cancer is associated with pericyte dysfunction
Molecular Oncology, EarlyView.Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero +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
MPSUBoost: A Modified Particle Stacking Undersampling Boosting Method
IEEE Access, 2022 Class imbalance problems are prevalent in the real world. In such cases, traditional supervised algorithms tend to have difficulty in recognizing minority data because the models are likely to maximize prediction accuracy by simply ignoring minority data.
Sang-Jin Kim, Dong-Joon Lim
doaj +1 more source
, 2017 Lung nodule classification is a class imbalanced problem, as nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority ...
Nakano, Hiroki +3 more
core +1 more source
Mine Classification With Imbalanced Data [PDF]
IEEE Geoscience and Remote Sensing Letters, 2009 In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly.
D.P. Williams, V. Myers, M.S. Silvious
openaire +1 more source
Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
wiley +1 more source