Results 41 to 50 of about 141,868 (281)

Survey on deep learning with class imbalance

Journal of Big Data, 2019
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real ...
Justin M. Johnson, Taghi M. Khoshgoftaar
doaj   +1 more source

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Metabolites, 2022
Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance.
Mehak Arora, Stephen C. Zambrzycki, Joshua M. Levy, Annette Esper, Jennifer K. Frediani, Cassandra L. Quave, Facundo M. Fernández, Rishikesan Kamaleswaran   +7 more
doaj   +1 more source

Class Rectification Hard Mining for Imbalanced Deep Learning

, 2017
Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Dong, Qi, Gong, Shaogang, Zhu, Xiatian
core   +1 more source

Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies [PDF]

, 2015
Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life.
Javidi, Mohammad Masoud, Shamsezat, Fatemeh   +1 more
core   +3 more sources

Observation points classifier ensemble for high‐dimensional imbalanced classification

CAAI Transactions on Intelligence Technology, 2023
In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extraction technique ...
Yulin He, Xu Li, Philippe Fournier‐Viger, Joshua Zhexue Huang, Mianjie Li, Salman Salloum   +5 more
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, Farid, Dewan Md., Jani, Md. Rafsan, Mahbub, Asif, Rayhan, Farshid, Shatabda, Swakkhar   +5 more
core   +1 more source

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

, 2020
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (
Carver, Eric, Chetty, Indrin, Elshaikh, Mohamed, Feldman, Aharon, Hearshen, David, Janic, Branislava, Lee, Joon, Liu, Chang, Movsas, Benjamin, Pantelic, Milan, Wen, Ning, Zong, Weiwei   +11 more
core   +1 more source

Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets [PDF]

, 2016
Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections.
Bettinger, Franck, Boechat, Andre, Dolberg, Lautaro, Duarte, Diogo, Glauner, Patrick O., Rangoni, Yves, State, Radu   +6 more
core   +2 more sources

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, Farid, Dewan Md., Jani, Md. Rafsan, Mahbub, Asif, Rahman, Chowdhury Mofizur, Rayhan, Farshid, Shatabda, Swakkhar   +6 more
core   +1 more source

Metric Learning from Imbalanced Data [PDF]

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points. Given a task, such a function can be optimized with a metric learning algorithm. Although this research field has received a lot of attention during the past decade, very few approaches have focused on learning a metric in an ...
Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban   +3 more
openaire   +2 more sources