Experimental Perspectives on Learning from Imbalanced Data

Experimental Perspectives on Learning from Imbalanced Data Presenter : Ai-Chen Liao Authors : Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano 2007 . ACM . Page : 935 - 942

Outline • Motivation • Objective • Method • Experimental Result • Conclusion • Comments

Motivation • When classes are imbalanced, many leaning algorithms can suffer from the perspective of reduced performance. • Can data sampling be used to improve the performance of learners built from imbalanced data? • Is the effectiveness of sampling related to the type of learner? • Do the results change if the objective is to optimize different performance metrics?

Objective • The objective of this research is to provide practical guidance to machine learning practitioners when building classifiers from imbalanced data, and to present to researchers some possible directions for future study.

Method • random undersampling (RUS) • random oversampling (ROS) • one-sided selection (OSS) • cluster-based oversampling (CBOS) • Wilson’s editing (WE) • SMOTE (SM) • borderline-SMOTE (BSM).

Experimental Results 214筆資料

Experimental Results

Conclusion • We have presented a comprehensive and systematic experimental analysis of learning from imbalanced data, using 11 learning algorithms with 35 real-world benchmark datasets from a variety of application domains. • Sampling is often critical to improving classifier performance, especially optimizing threshold-dependent measures such as the geometric mean or TPR.

Comments • Advantage • A lot of experiments • Drawback • … • Application • Handling imbalanced data

Experimental Perspectives on Learning from Imbalanced Data