ICML 2003 Workshop -- Accepted Papers
Learning from Imbalanced Data Sets II
List of Accepted Papers
- Chris Drummond, Robert Holte, C4.5, Class Imbalance, and Cost Sensitivity:
Why Under-Sampling beats Over-Sampling
- Nitesh Chawla, C4.5 and Imbalanced Datasets: Investigating the effect of
sampling method, probabilistic estimate, and decision tree structure.
- Nathalie Japkowicz, Class Imbalance: Are we focusing on the right issue?
- Aleksandar Kolcz, Abdur Chowdhury, Joshua Alspector, Data duplication: An
Imbalance Problem
- Zhaohui Zheng, Rohini Srihari, Optimally Combining Positive and Negative
Features for Text Categorization
- Jianping Zhang, Inderjeet Mani, kNN Approach to Unbalanced Data
Distributions: A Case Study involving Information Extraction
- Gang Wu, Edward Y. Chang, Class-Boundary Alignment for Imbalanced Dataset
Learning
- Bhavani Raskutti, Adam Kowalczyk, Extreme Re-balancing for SVM's: a case
study
- Ronald Pearson, Gregory Goney, James Shwaber, Imbalanced Clustering for
Microarray Time-Series
- Marcus Maloof, Learning When Data Sets are Imbalanced and When Costs are
Unequal and Unknown
- Piotr Juszczak, Robert P.W. Duin, Uncertainty sampling methods for
one-class classifiers
- Ray Hickey, Learning Rare Class Footprints: the REFLEX Algorithm
- Sofia Visa, Anca Ralescu, Learning Imabalnaced and Overlapping Classes
using Fuzzy Sets