Abstract:
Classification in imbalanced domains is a challenging task, since most of its real domain applications present skewed distributions of data. However, there are still some...Show MoreMetadata
Abstract:
Classification in imbalanced domains is a challenging task, since most of its real domain applications present skewed distributions of data. However, there are still some open issues in this kind of problem. This paper presents a multi-objective grammar-based ant programming algorithm for imbalanced classification, capable of addressing this task from both the binary and multiclass sides, unlike most of the solutions presented so far. We carry out two experimental studies comparing our algorithm against binary and multiclass solutions, demonstrating that it achieves an excellent performance for both binary and multiclass imbalanced data sets.
Published in: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)
Date of Conference: 27-29 November 2012
Date Added to IEEE Xplore: 24 January 2013
ISBN Information: