.png){kind=icon}
返回
Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2017.2682249.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There have been many attempts to classify imbalanced data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. Many conventional methods, which can be categorized into sampling, cost-sensitive, or ensemble, include heuristic and task dependent processes. In order to achieve a better classification performance by formulation without heuristics and task dependence, we propose confusion-matrix-based kernel logistic regression (CM-KLOGR). Its objective function is the harmonic mean of various evaluation criteria derived from a confusion matrix, such criteria as sensitivity, positive predictive value, and others for negatives. This objective function and its optimization are consistently formulated on the framework of KLOGR, based on minimum classification error and generalized probabilistic descent (MCE/GPD) learning. Due to the merits of the harmonic mean, KLOGR, and MCE/GPD, CM-KLOGR improves the multifaceted performances in a well-balanced way. This paper presents the formulation of CM-KLOGR and its effectiveness through experiments that comparatively evaluated CM-KLOGR using benchmark imbalanced datasets.
Keyword:
Imbalanced data
confusion matrix
kernel logistic regression
minimum classification error and generalized probabilistic descent
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
Cubic-quartic optical soliton perturbation with Fokas–Lenells equation by semi-inverse variation
OAAI
OAAI分享收藏
分享收藏分享收藏Facile Synthesis of Phosphate‐Functionalized MWCNT–TiO2 Nanocomposites as Efficient Photocatalysts and Insights into the Roles of Nanostructured Carbon.png)
ChemPlusChem
ChemPlusChemIF0
PREAI
分享收藏
查看更多内容