Class Imbalance Problem in Data Mining Review

By Rushi Longadge, Snehalata Dongre. arXiv.org, 2013

576 citations recorded by the source corpus.

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, data-preprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.

Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a553f2ed1f7e89af81e7d0175d20ebb86c07a816

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “Although, re-sampling techniques achieves success in imbalanced data classification, a hybrid method of combining sampling techniques with Bagging is proposed by Lu et al. (2016). The experiment is performed on 26 benchmark datasets that showed that sampling methods outperforms bagging whereas baggi”
A comprehensive data level analysis for cancer diagnosis on imbalanced data (2019, methodology): “However, this design principle results in errors in the classifications of the minority class [18].”
Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, background): “Sampling, explained at [42], is a simple yet powerful technique to overcome this problem.”
RetinaNet With Difference Channel Attention and Adaptively Spatial Feature Fusion for Steel Surface Defect Detection (2020, background): “RetinaNet [24] created the focal loss to reduce the impact of class imbalance [25] on recognition accuracy.”
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, background): “Moreover, scientists are often called to face what is often referred to as the imbalance problem [85].”
Classification with class imbalance problem: A review (2015, background): “However, it is pointed out by many such as [74], [66] and [75] that sampling might not be enough to solve the challenge of overlapping problem in class imbalance data.”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, methodology): “At present, the data-level method is mainly in the data pre-processing stage, using resampling to redistribute the training data of different classes in the data space [19], [20].”