Meta-learning for imbalanced data and classification ensemble in binary classification

Authors:
Sung-Chiang Lin;Yuan-chin I. Chang;Wei-Ning Yang
Affiliations:
Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan;Institute of Statistical Science, Academia Sinica, Taipei, Taiwan;Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan
Venue:
Neurocomputing
Year:
2009

Citing 16
Cited 2

Applied multivariate analysis

Applied multivariate analysis
Applied multivariate techniques

Applied multivariate techniques
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A perspective view and survey of meta-learning

Artificial Intelligence Review
Introduction to the Special Issue on Meta-Learning

Machine Learning
Is Combining Classifiers with Stacking Better than Selecting the Best One?

Machine Learning
Editorial: special issue on learning from imbalanced data sets

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Exploratory Under-Sampling for Class-Imbalance Learning

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Classifying imbalanced data using a bagging ensemble variation (BEV)

ACM-SE 45 Proceedings of the 45th annual southeast regional conference
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
Issues in stacked generalization

Journal of Artificial Intelligence Research
Learning from imbalanced data in surveillance of nosocomial infection

Artificial Intelligence in Medicine
Online rare events detection

PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
z-SVM: an SVM for improved classification of imbalanced data

AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence

EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

Neurocomputing
Adjusted F-measure and kernel scaling for imbalanced data learning

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

To conduct binary classification with highly imbalanced data is a very common problem, especially when the examples of interest are relatively rare. In this paper, we proposed the ''Meta Imbalanced Classification Ensemble (MICE)'' algorithm in order to dilute the effect of imbalanced data. In the MICE, the majority group is partitioned based on the transformed features from ''inner product'' to retain the geometric relation between two groups. The empirical results show that the performance of MICE is better than some renowned classification methods in terms of the specificity and the sensitivity.