DocumentCode :
2770735
Title :
Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm
Author :
Jeatrakul, Piyasak ; Wong, Kok Wai
Author_Institution :
Sch. of Inf. Technol., Mae Fah Luang Univ., Chiang Rai, Thailand
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
8
Abstract :
In data classification, the problem of imbalanced class distribution has attracted many attentions. Most efforts have used to investigate the problem mainly for binary classification. However, research solutions for the imbalanced data on binary-class problems are not directly applicable to multi-class applications. Therefore, it is a challenge to handle the multi-class problem with imbalanced data in order to obtain satisfactory results. This problem can indirectly affect how human visualise the data. In this paper, an algorithm named One-Against-All with Data Balancing (OAA-DB) is developed to enhance the classification performance in the case of the multi-class imbalanced data. This algorithm is developed by combining the multi-binary classification technique called One-Against-All (OAA) and a data balancing technique. In the experiment, the three multi-class imbalanced data sets used were obtained from the University of California Irvine (UCI) machine learning repository. The results show that the OAA-DB algorithm can enhance the classification performance for the multi-class imbalanced data without reducing the overall classification accuracy.
Keywords :
learning (artificial intelligence); pattern classification; OAA-DB algorithm; UCI; University of California Irvine machine learning repository; binary-class problems; classification performance enhancement; data classification; multi-class applications; multiclass imbalanced data; one-against-all with data balancing; Accuracy; Artificial neural networks; Classification algorithms; Educational institutions; Glass; Training; OAA; SMOTE; artificial neural network; classification; complementary neural network; misclassification analysis; multi-class imbalanced data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252450
Filename :
6252450
Link To Document :
بازگشت