.png){kind=icon}
返回
Multinomial classification with class-conditional overlapping sparse feature groups
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2017.11.002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Regularized multinomial logistic model is widely used in multi-class classification problems. For high dimension data, various regularization methods achieving sparsity have been developed and applied successfully to many real-world applications such as bioinformatics, health informatics and text mining. In many cases there exist intrinsic group structures among the features. Incorporating the group information in the model can enhance model performance. In multi-class classification, different classes may relate to different feature groups. With these considerations, we propose a class-conditional regularization of the multinomial logistic model (CCSOGL) to enable the discovery of class-specific feature groups. To solve the model, we developed an efficient cyclic block coordinate descent based algorithm. We also apply our method to analyze real-world datasets to demonstrate its superior performance. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Multinomial classification
Class-conditional feature group
Feature selection
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:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series在TRANSBIG多中心独立验证系列中，淋巴结阴性乳腺癌患者76基因预后签名的强时间依赖性.png)
CLINICAL CANCER RESEARCHOAAI
CLINICAL CANCER RESEARCHIF10.2
OAAI分享收藏
分享收藏THE LEUKOCYTE COUNT: ASSOCIATIONS WITH INTENSITY OF SMOKING AND PERSISTENCE OF EFFECT AFTER QUITTING
PREAI
分享收藏
查看更多内容