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Introduction

Classification is an important problem in machine learning. It can be used in a variety of applications, such as separating apples, oranges, and bananas automatically. Traditionally, the regular classification setup aims at minimizing the rate of future mis-prediction errors. Nevertheless, in some applications, it is needed to treat different types of mis-prediction errors differently. For instance, in a medical decision system, the cost of mis-predicting a cancerous patient as a healthy one may be higher than the other way around. In an animal recognition system, the silliness of mis-predicting a person as a fish may be higher than the silliness of mis-predicting her/him as a monkey. Such a need can be formalized as the cost-sensitive classification setup, which is drawing much research attention because of its many potential applications, including targeted marketing, fraud detection, and web analysis.

Our Related Works

paper

  • Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin and Hsuan-Tien Lin, A Simple Methodology of Soft Cost-sensitive Classification. In Proceedings of KDD '12, 2012. link
  • Te-Kang Jan, Hsuan-Tien Lin, Hsin-Pai Chen, Tsung-Chen Chern, Chung-Yueh Huang, Bing-Cheng Wen, Chia-Wen Chung, Yung-Jui Li, Ya-Ching Chuang, Li-Li Li, Yu-Jiun Chan, Juen-Kai Wang, Yuh-Lin Wang, Chi-Hung Lin and Da-Wei Wang. Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering, BIBM 2011. link
  • Hsuan-Tien Lin. A Simple Cost-sensitive Multiclass Classification Algorithm Using One-versus-one Comparisons, National Taiwan University, December 2010. link
  • Hsuan-Tien Lin. Cost-sensitive Classification: Status and Beyond. Workshop on Machine Learning Research in Taiwan: Challenges and Directions @ TAAI, 2010. link
  • Han-Hsing Tu and Hsuan-Tien Lin. One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. In Proceedings of ICML '10, 1095-1102, 2010. link

thesis

  • Po-Lung Chen. Active Learning for Multiclass Cost-sensitive Classification Using Probabilistic Models. Master's Thesis, 2012.
  • Te-Kang Jan. A Comparison of Methods for Cost-sensitive Support Vector Machines. Master's Thesis, 2010.
  • Hanhsing Tu. Regression Approaches for Multi-class Cost-sensitive Classification. Master's Thesis, 2009.
public/cost-sensitive_classification.txt · Last modified: 2013/07/07 06:02 by htlin