Výsledky vyhledávání

Report

SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated

Autor: Deng, Jiahao, Brown, Eli T.

Clustering analysis is one of the critical tasks in machine learning. Traditionally, clustering has been an independent task, separate from outlier detection. Due to the fact that the performance of clustering can be significantly eroded by outliers,

Externí odkaz: http://arxiv.org/abs/2208.05561

Zobrazit plný text záznamu

Report

Synthetic Sampling for Multi-Class Malignancy Prediction

Autor: Yung, Matthew, Brown, Eli T., Rasin, Alexander, Furst, Jacob D., Raicu, Daniela S.

We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for o

Externí odkaz: http://arxiv.org/abs/1807.02608

Zobrazit plný text záznamu

RISSAD: Rule-based Interactive Semi-Supervised Anomaly Detection

Autor: Deng, Jiahao, Brown, Eli T.

Anomaly detection has gained increasing attention from researchers in recent times. Owing to a lack of reliable ground-truth labels, many current state-of-art techniques focus on unsupervised learning, which lacks a mechanism for user involvement. Fu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a0da2df8dd55925cc073479d56951540

Zobrazit plný text záznamu