29 citations · 65 across the 21 of their papers we have counts for
12 papers · 1 filter
Learning Acceptance Regions for Many Classes with Anomaly Detection
Zhou Wang, Xingye Qiao
Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance…
Covariance-engaged Classification of Sets via Linear Programming
Zhao Ren, Sungkyu Jung, Xingye Qiao
Set classification aims to classify a set of observations as a whole, as opposed to classifying individual observations separately. To formally understand the unfamiliar concept of…
Near-optimal Individualized Treatment Recommendations
Haomiao Meng, Ying-Qi Zhao, Haoda Fu +1
Individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal is to assign proper treatments to patients based on their individu…
Rates of Convergence for Large-scale Nearest Neighbor Classification
Xingye Qiao, Jiexin Duan, Guang Cheng
Nearest neighbor is a popular class of classification methods with many desirable properties. For a large data set which cannot be loaded into the memory of a single machine due to…
Learning Confidence Sets using Support Vector Machines
Wenbo Wang, Xingye Qiao
The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outsi…
On Reject and Refine Options in Multicategory Classification
Chong Zhang, Wenbo Wang, Xingye Qiao
In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision unti…
Significance Analysis of High-Dimensional, Low-Sample Size Partially Labeled Data
Qiyi Lu, Xingye Qiao
Classification and clustering are both important topics in statistical learning. A natural question herein is whether predefined classes are really different from one another, or w…
Sparse Fisher's Linear Discriminant Analysis for Partially Labeled Data
Qiyi Lu, Xingye Qiao
Classification is an important tool with many useful applications. Among the many classification methods, Fisher's Linear Discriminant Analysis (LDA) is a traditional model-based a…
Noncrossing Ordinal Classification
Xingye Qiao
Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We conside…
Stabilized Nearest Neighbor Classifier and Its Statistical Properties
Wei Sun, Xingye Qiao, Guang Cheng
The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of scientific method. It entails that similar statistical conclusio…
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
Xingye Qiao, Lingsong Zhang
Classification is an important topic in statistics and machine learning with great potential in many real applications. In this paper, we investigate two popular large margin class…
Distance-weighted Support Vector Machine
Xingye Qiao, Lingsong Zhang
A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article.…