# Smote: Synthetic Minority Over-Sampling Technique

Nitesh V. Chawla **chawla@csee.usf.edu**
Department of Computer Science and Engineering, ENB 118 University of South Florida 4202 E. Fowler Ave. Tampa, FL 33620-5399, USA
Kevin W. Bowyer **kwb@cse.nd.edu**
Department of Computer Science and Engineering 384 Fitzpatrick Hall University of Notre Dame Notre Dame, IN 46556, USA
Lawrence O. Hall **hall@csee.usf.edu**
Department of Computer Science and Engineering, ENB 118 University of South Florida 4202 E. Fowler Ave. Tampa, FL 33620-5399, USA
W. Philip Kegelmeyer **wpk@california.sandia.gov**
Sandia National Laboratories Biosystems Research Department, P.O. Box 969, MS 9951 Livermore, CA, 94551-0969, USA

## Abstract

An approach to the construction of classifiers from imbalanced datasets is described.

A dataset is imbalanced if the classification categories are **not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples**
with only a small percentage of "abnormal" or "interesting" **examples. It is also the case** that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the **sensitivity of a classifier to**
the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class.

This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors **in Naive Bayes. Our method**
of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

## 1. Introduction

A dataset is imbalanced if the classes are not approximately **equally represented. Imbalance** on the order of 100 to 1 is prevalent in fraud detection and imbalance of up to 100,000 to c 2002 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.