Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification
高维分类中的非参数特征增广与选择：FANSAI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究通用高维分类，逻辑回归用于分类而非明确的经济离散选择模型。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
高维分类中的非参数特征增广与选择：FANS
- 研究问题
- 面对大量候选特征，如何利用单变量密度信息构造灵活的非线性分类边界，同时控制模型复杂度与计算负担？
- 方法与思路
- 作者先估计边际密度比以变换或增广特征，再进行惩罚逻辑回归，并通过联合密度对数比的表示联系朴素贝叶斯模型。
- 材料陈述的贡献
- 摘要称建立FANS的风险界，并在模拟、垃圾邮件与基因表达数据中展示竞争性表现，同时提出利用并行计算的快速算法。
- 阅读关注点
- 应核对密度比估计、特征增广和惩罚选择的具体步骤，风险界所需条件，以及非线性分类与计算优势的比较基准。
- 阅读边界
- 依据arXiv 1401.0211v2预印本摘要，不能据此声称已同行评审或期刊接收；风险界条件、数值幅度及刊本变化未说明。
原文摘要
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.