HiBBKA: A Hybrid Method With Resampling and Heuristic Feature Selection for Class‐Imbalanced Data in Chemometrics
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Chemometrics"
DOI: 10.1002/cem.70029
Abstract: In critical domains including medicinal chemistry, biomedicine, metabolomics, and computational toxicology, class imbalance in datasets and poor recognition accuracy for minority classes remain persistent challenges. While previous studies have employed resampling and feature selection techniques… read more here.
Keywords: imbalanced data; rbu smote; feature selection; class ... See more keywords
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
Understanding imbalanced data: XAI & interpretable ML framework
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DOI: 10.1007/s10994-023-06414-w
Abstract: There is a gap between current methods that explain deep learning models that work on imbalanced image data and the needs of the imbalanced learning community. Existing methods that explain imbalanced data are geared toward… read more here.
Keywords: understanding imbalanced; xai interpretable; imbalanced learning; framework ... 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
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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
Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data
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DOI: 10.1021/acsomega.4c09719
Abstract: The earthworm is a key indicator species in soil ecosystems. This makes the reproductive toxicity of chemical compounds to earthworms a desired property of determination and makes computational models necessary for descriptive and predictive purposes.… read more here.
Keywords: reproductive toxicity; imbalanced data; predictive modeling; toxicity ... See more keywords