Handling data irregularities in classification: Foundations, trends, and future challenges

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs

Class imbalance learning is a recent topic, which helps us to detect the classes from unbalanced datasets. In various real scenarios, where we need to find the exceptional cases like credit card problem, brain tumor detection, etc., the traditional classification algorithms fail because they are designed in such a way that either their results are overwhelmed by the bigger class and or they ignore the smaller class as a noise and avoid it. In recent studies, it has been found that class imbalance problem itself is not a problem but there are certain other data distribution complexities, which when combined with the class imbalance problem degrade the performance of classifier. One of the major issues is noise in the data, which is a part of every real data in one form or another. This paper compares the oversampling and undersampling approaches of class imbalance learning in noisy environment and tries to find out which is the better approach in such case.

Comparing the Behavior of Oversampling and Undersampling Approach of Class Imbalance Learning by Combining Class Imbalance Problem with Noise

A robust fuzzy least squares twin support vector machine for class imbalance learning

Incident reporting and investigation are components of safety management systems. Timely and accurate identification of risk factors is crucial to effective prevention strategies. However, risk fac...

A Data-Mining Approach to Identification of Risk Factors in Safety Management Systems

Imbalanced datasets are frequently found in many real applications. Resampling is one of the effective solutions due to generating a relatively balanced class distribution. In this paper, a hybrid sampling SVM approach is proposed combining an oversampling technique and an undersampling technique for addressing the imbalanced data classification problem. The proposed approach first uses an undersampling technique to delete some samples of the majority class with less classification information and then applies an oversampling technique to gradually create some new positive samples. Thus, a balanced training dataset is generated to replace the original imbalanced training dataset. Finally, through experimental results on the real-world datasets, our proposed approach has the ability to identify informative samples and deal with the imbalanced data classification problem.

/pdf/a-hybrid-sampling-svm-approach-to-imbalanced-data-4vlls5k7oz.pdf

A Hybrid Sampling SVM Approach to Imbalanced Data Classification

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Qiang Wang

Author Tools

Chat about Author

Papers

A Hybrid Sampling SVM Approach to Imbalanced Data Classification