首页|Eotvos Lorand University (ELTE) Researchers Describe Advances in Intelligent Sys tems (Cluster-based oversampling with area extraction from representative points for class imbalance learning)
Eotvos Lorand University (ELTE) Researchers Describe Advances in Intelligent Sys tems (Cluster-based oversampling with area extraction from representative points for class imbalance learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on intelligent systems are presented in a new report. According to news reporting from Budapest, Hungary, b y NewsRx journalists, research stated, “Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class.” The news reporters obtained a quote from the research from Eotvos Lorand Univers ity (ELTE): “Resampling methods have been used to adjust the class distribution, but they often have limitations for small disjunct minority subsets. This paper introduces AROSS, an adaptive cluster-based oversampling approach that addresse s these limitations. AROSS utilizes an optimized agglomerative clustering algori thm with the Cophenetic Correlation Coefficient and the Bayesian Information Cri terion to identify representative areas of the minority class. Safe and half-saf e areas are obtained using an incremental k-Nearest Neighbor strategy, and overs ampling is performed with a truncated hyperspherical Gaussian distribution.” According to the news editors, the research concluded: “Experimental evaluations on 70 binary datasets demonstrate the effectiveness of AROSS in improving class imbalance learning performance, making it a promising solution for mitigating c lass imbalance challenges, especially for small disjunct minority subsets.”
Eotvos Lorand University (ELTE)Budapes tHungaryEuropeIntelligent SystemsMachine Learning
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