A Systematic Review on Imbalanced Data Challenges in Machine Learning :  Applications and Solutions

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

Related collections

Most cited references 132

Record: found
Abstract: not found
Article: not found

An introduction to ROC analysis

Tom Fawcett (2006)

0 comments Cited 1919 times – based on 0 reviews      Review now

Record: found
Abstract: not found
Article: not found

Learning from Imbalanced Data

Haibo He, E.A. Garcia (2009)

0 comments Cited 769 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

A review of feature selection techniques in bioinformatics.

Yvan Saeys, Iñaki Inza, Pedro Larrañaga (2007)

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.