Next Article in Journal
Visual Saliency Model-Based Image Watermarking with Laplacian Distribution
Previous Article in Journal
On Homomorphism Theorem for Perfect Neutrosophic Extended Triplet Groups
Article Menu

Export Article

Open AccessArticle
Information 2018, 9(9), 238; https://doi.org/10.3390/info9090238

Imbalanced Learning Based on Data-Partition and SMOTE

School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Received: 5 August 2018 / Revised: 13 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
(This article belongs to the Section Artificial Intelligence)
Full-Text   |   PDF [905 KB, uploaded 19 September 2018]   |  

Abstract

Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method based on data-partition and SMOTE for imbalanced learning. The proposed method differs from conventional ones in both the learning and prediction stages. For the learning stage, the proposed method uses the following three steps to learn a class-imbalance oriented model: (1) partitioning the majority class into several clusters using data partition methods such as K-Means, (2) constructing a novel training set using SMOTE on each data set obtained by merging each cluster with the minority class, and (3) learning a classification model on each training set using convention classification learning methods including decision tree, SVM and neural network. Therefore, a classifier repository consisting of several classification models is constructed. With respect to the prediction stage, for a given example to be classified, the proposed method uses the partition model constructed in the learning stage to select a model from the classifier repository to predict the example. Comprehensive experiments on KEEL data sets show that the proposed method outperforms some other existing methods on evaluation measures of recall, g-mean, f-measure and AUC. View Full-Text
Keywords: data-partition; imbalanced learning; SMOTE data-partition; imbalanced learning; SMOTE
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Guo, H.; Zhou, J.; Wu, C.-A. Imbalanced Learning Based on Data-Partition and SMOTE. Information 2018, 9, 238.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top