Results 31 to 40 of about 527,011 (237)
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
, 2017 Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced.
Ahmed, Sajid +6 more
core +1 more source
Techno.Com, 2023 Churn merupakan kondisi dimana seseorang berpindah dari satu layanan ke layanan yang lain. Churn pelanggan menjadi masalah yang meningkat cukup signifikan dan menjadi tantangan utama yang harus dihadapi banyak perusahaan perbankan karena memiki peran ...
Wahyu Nugraha, Muhamad Syarif
doaj +1 more source
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
, 2017 Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature.
Ahmed, Sajid +5 more
core +1 more source
Class imbalance impact on the prediction of complications during home hospitalization: a comparative study. [PDF]
, 2019 © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new ...
Calvo González, Mireia +6 more
core +1 more source
Effects of Class Imbalance Countermeasures on Interpretability
IEEE AccessThe widespread use of artificial intelligence (AI) in more and more real-world applications is accompanied by challenges that are not obvious at first glance. In machine learning, class imbalance, characterized by an imbalance in the frequency of classes,
David Cemernek +2 more
doaj +1 more source
Lattice and Imbalance Informed Multi-Label Learning
IEEE Access, 2020 In a multi-label dataset, an instance is given a single representation across all possible labels. Despite the mutual sharing of instances among the labels, the membership of the instances vary from label to label.
Payel Sadhukhan, Sarbani Palit
doaj +1 more source
Misclassification analysis for the class imbalance problem [PDF]
, 2010 In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored.
Fung, C.C. +3 more
core +1 more source
Deep Over-sampling Framework for Classifying Imbalanced Data
, 2017 Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured
B Krawczyk +15 more
core +1 more source
LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test [PDF]
, 2018 In this paper, we introduce a new likelihood ratio imbalance degree (LRID) to measure the class-imbalance extent of multi-class data. Imbalance ratio (IR) is usually used to measure class-imbalance extent in imbalanced learning problems.
Ma, Z. +4 more
core +1 more source
Rapid Response to Trametinib Combined With Chemotherapy for Infant BRAF‐Fused Chiasmatic Glioma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Infants, less than 1 year, with chiasmatic gliomas (ICG) present a major therapeutic challenge due to large tumor size, decreased vision, rapid progression, and poor response to vincristine/carboplatin chemotherapy. The majority have a BRAF fusion, which may respond to downstream MEK inhibitors but response time is slow. There are no safety or
Helen Toledano +7 more
wiley +1 more source