Title
Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems
Abstract
The paper deals a novel resampling method in order to cope with imbalanced dataset in binary classification problems. Imbalanced datasets are frequently found in many industrial applications: for instance, the occurrence of particular product defects or machine faults are rare events whose detection is of utmost importance. In this paper a new resampling method combining an oversampling and an undersampling techniques is treated. In order to prove the effectiveness of the proposed approach, several tests have been developed. Two classifiers based on Support Vector Machine and Decision Tree have been designed, which are applied for binary classification on four datasets: a synthetic dataset, a widely used public dataset and two industrial datasets. The obtained results are presented and discussed in the paper; in particular, the performance that is achieved by the two classifiers through our resampling approach is compared to the ones that are obtained without any resampling and through the classical SMOTE approach, respectively.
Year
DOI
Venue
2011
10.1109/ISDA.2011.6121689
Intelligent Systems Design and Applications
Keywords
Field
DocType
decision trees,pattern classification,sampling methods,support vector machines,binary classification,decision tree,imbalanced dataset classification,industrial dataset,industrial problem,oversampling technique,public dataset,real world problem,resampling method,support vector machine,synthetic dataset,undersampling technique,imbalanced dataset,oversampling,undersampling
Decision tree,Data mining,Binary classification,Computer science,Artificial intelligence,Resampling,Rare events,Oversampling,Pattern recognition,Support vector machine,Undersampling,Sampling (statistics),Machine learning
Conference
ISSN
ISBN
Citations 
2164-7143
978-1-4577-1676-8
1
PageRank 
References 
Authors
0.36
6
3
Name
Order
Citations
PageRank
Silvia Cateni110.36
Valentina Colla215929.50
Marco Vannucci39415.60