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Proceedings Paper

Imbalanced learning for pattern recognition: an empirical study
Author(s): Haibo He; Sheng Chen; Hong Man; Sachi Desai; Shafik Quoraishee
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Paper Abstract

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Paper Details

Date Published: 28 October 2010
PDF: 7 pages
Proc. SPIE 7833, Unmanned/Unattended Sensors and Sensor Networks VII, 78330T (28 October 2010); doi: 10.1117/12.867737
Show Author Affiliations
Haibo He, The Univ. of Rhode Island (United States)
Sheng Chen, Stevens Institute of Technology (United States)
Hong Man, Stevens Institute of Technology (United States)
Sachi Desai, U.S. Army Armament Research, Development and Engineering Ctr. (United States)
Shafik Quoraishee, U.S. Army Armament Research, Development and Engineering Ctr. (United States)


Published in SPIE Proceedings Vol. 7833:
Unmanned/Unattended Sensors and Sensor Networks VII
Edward M. Carapezza, Editor(s)

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