Paper Info
Title
The Performance of Allocation Method on Imbalanced Data.
Abstract
This paper presents results of classification on imbalanced data with ensemble allocation method. The result of the allocation method were compared to traditional techniques for dealing with imbalaced datasets - the sampling methods. The allocation method is a two level ensemble that combines unsupervised and supervised learning. In this research the first level of allocation the unsupervised anomaly detection is used as an allocator which is combined with several traditional classification method on second level of ensemble. The allocation method is tested on imbalanced datasets and the results are compared to two well used sampling methods - under-sampling of majority instances, and over-sampling with SMOTE which introduces new artificial instances of minority class to the dataset. Results of all of the methods were compared on overall accuracy and average F-score metrics. The results show that allocation method produces the best classification model, which is also supported by statistical analysis.
Year
DOI
Venue
2016
10.3233/978-1-61499-720-7-382
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
classification,sampling,allocation,imbalanced data
Discrete mathematics,Theoretical computer science,Mathematics
Conference
Volume
ISSN
Citations 
292
0922-6389
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Saso Karakatic1447.02
Marjan Hericko230544.16
Vili Podgorelec319933.00