A classification for complex imbalanced data in disease screening and early diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9442
Abstract: Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high‐dimensional longitudinal… read more here.
Keywords: classification; disease; diagnosis; imbalanced classification ... See more keywords
A design of information granule-based under-sampling method in imbalanced data classification
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-020-05023-2
Abstract: In numerous real-world problems, we are faced with difficulties in learning from imbalanced data. The classification performance of a “standard” classifier (learning algorithm) is evidently hindered by the imbalanced distribution of data. The over-sampling and… read more here.
Keywords: information granules; information; imbalanced data; data classification ... See more keywords
Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Intelligent Manufacturing"
DOI: 10.1007/s10845-020-01564-3
Abstract: One of the most important parameters in machining process is tool wear. Thus, monitoring the wear of cutting tools is essential to ensure product quality, increase productivity, reduce environmental impact and avoid catastrophic damages. As… read more here.
Keywords: imbalanced data; wear condition; tool; tool wear ... See more keywords
Distributed classification for imbalanced big data in distributed environments
Sign Up to like & getrecommendations! Published in 2021 at "Wireless Networks"
DOI: 10.1007/s11276-021-02552-y
Abstract: Recently, with the development of technology, it is quite important to study scalable computational methods for handling large-scale data in big data applications. The cloud/edge computing are powerful tools for solving big data problems, and… read more here.
Keywords: distributed classification; imbalanced data; classification imbalanced; imbalanced big ... See more keywords
Imbalanced Data Processing Model for Software Defect Prediction
Sign Up to like & getrecommendations! Published in 2018 at "Wireless Personal Communications"
DOI: 10.1007/s11277-017-5117-z
Abstract: In the field of software engineering, software defect prediction is the hotspot of the researches which can effectively guarantee the quality during software development. However, the problem of class imbalanced datasets will affect the accuracy… read more here.
Keywords: imbalanced data; defect prediction; model; software defect ... See more keywords
KNN-based maximum margin and minimum volume hyper-sphere machine for imbalanced data classification
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-017-0720-6
Abstract: Imbalanced data classification is often met in our real life. In this paper, a novel k-nearest neighbor (KNN)-based maximum margin and minimum volume hyper-sphere machine (KNN-M3VHM) is presented for the imbalanced data classification. The basic… read more here.
Keywords: machine; imbalanced data; hyper; hyper sphere ... See more keywords
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
Sign Up to like & getrecommendations! Published in 2021 at "ISA transactions"
DOI: 10.1016/j.isatra.2021.02.042
Abstract: The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault… read more here.
Keywords: fault; imbalanced data; research; diagnosis ... See more keywords
Deep Learning-Based Imbalanced Data Classification for Drug Discovery
Sign Up to like & getrecommendations! Published in 2020 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.9b01162
Abstract: Drug discovery studies have become increasingly expensive and time-consuming processes. In the early phase of drug discovery studies, an extensive search has been performed to find drug-like compounds, which then can be optimized over time… read more here.
Keywords: classification; imbalanced data; drug; performance ... See more keywords
A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection
Sign Up to like & getrecommendations! Published in 2019 at "Connection Science"
DOI: 10.1080/09540091.2018.1512558
Abstract: ABSTRACT Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the… read more here.
Keywords: classification; imbalanced data; feature; pattern matching ... See more keywords
A Hybrid GAN Based Approach to Solve Imbalanced Data Problem in Recommendation Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3141776
Abstract: With the advent of information technology, the amount of online data generation has been massive. Recommendation systems have become an effective tool in filtering information and solving the problem of information overload. Machine learning algorithms… read more here.
Keywords: problem; approach solve; recommendation; recommendation systems ... See more keywords
An Improved Extreme Learning Machine for Imbalanced Data Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3142724
Abstract: In the field of machine learning, Extreme Learning Machine (ELM) has been widely used in classification and regression tasks. However, like many traditional machine learning algorithms, the classification results of ELM are often not good… read more here.
Keywords: extreme learning; machine; elm; learning machine ... See more keywords