4 citations · 6 across the 6 of their papers we have counts for
6 papers
Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
Junhyung Kim, Byungyoon Park, Charmgil Hong
We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label depend…
Detection of Abnormal Input-Output Associations
Charmgil Hong, Siqi Liu, Milos Hauskrecht
We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output…
Detecting Unusual Input-Output Associations in Multivariate Conditional Data
Charmgil Hong, Milos Hauskrecht
Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to un…
Dealing with Class Imbalance using Thresholding
Charmgil Hong, Rumi Ghosh, Soundar Srinivasan
We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tuna…
MCODE: Multivariate Conditional Outlier Detection
Charmgil Hong, Milos Hauskrecht
Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent…
A Mixtures-of-Experts Framework for Multi-Label Classification
Charmgil Hong, Iyad Batal, Milos Hauskrecht
We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-st…