Learning in the Presence of Class Imbalance and Concept Drift

  in Special Issue   Posted on August 13, 2017

Information for the Special Issue

Submission Deadline: Mon 23 Oct 2017
Journal Impact Factor : 4.438
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/neurocomputing/call-for-papers/special-issue-on-learning-in-the-presence-of-class-imbalance
Journal & Submission Website: https://www.journals.elsevier.com/neurocomputing

Special Issue Call for Papers:

Guest Editors: Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao

Managing Guest Editor: Xin Yao

1. Introduction

With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Applications in various domains such as risk management, anomaly detection, fraud detection, software engineering, social media mining, and recommender systems are affected by both class imbalance and concept drift. Class imbalance happens when the data categories are not equally represented, i.e., at least one category is minority compared to other categories. It can cause learning bias towards the majority class and poor generalization. Concept drift is a change in the underlying distribution of the problem, and is a significant issue specially when learning from data streams. It requires learners to be adaptive to dynamic changes.

Class imbalance and concept drift can significantly hinder predictive performance, and the problem becomes particularly challenging when they occur simultaneously. This challenge arises from the fact that one problem can affect the treatment of the other. For example, drift detection algorithms based on the traditional classification error may be sensitive to the imbalanced degree and become less effective; and class imbalance techniques need to be adaptive to changing imbalance rates, otherwise the class receiving the preferential treatment may not be the correct minority class at the current moment. Therefore, the mutual effect of class imbalance and concept drift should be considered during algorithm design.

The aim of this special issue is to bring together the original work from the areas of class imbalance learning and concept drift in order to solve the combined issue of class imbalance and concept drift. In order to advance the state-of-the-art on the combined issue, it is important to also advance the state-of-the art in each individual area. Therefore, this special issue encourages submissions not only on the combined issue, but also on these two areas themselves.

2. Topics of Interest

The list of possible topics includes, but is not limited to:

(1) Research topics related to the combined issues of class imbalance and concept drift:

  • Concept drift detection in imbalanced data streams.
  • New data-level and algorithm-level approaches to dealing with class imbalance in non-stationary environments.
  • Semi-supervised learning and active learning approaches to dealing with imbalanced data streams.
  • Adaptive ensemble approaches for imbalanced data streams.
  • Performance evaluation on imbalanced data streams in incremental and online learning scenarios.
  • Case studies and real-world applications dealing with both class imbalance and concept drift.

(2) Research topics related to class imbalanced learning:

  • Data-level and algorithm-level techniques for imbalanced data.
  • Ensemble learning approaches for imbalanced data.
  • Cost-sensitive and cost-free learning approaches.
  • Imbalanced data with multiple classes or multiple labels.
  • Semi-supervised class imbalance learning.
  • Case studies and real-world applications dealing with class imbalanced data.

(3) Research topics related to learning in the presence of concept drift:

  • Passive and active approaches to dealing with concept drift.
  • Concept drift detection methods.
  • Chunk-based and online learning approaches for non-stationary environments.
  • Approaches to dealing with recurring concepts.
  • Adaptive ensemble approaches.
  • Semi-supervised learning in non-stationary environments.
  • Case studies and real-world applications involving concept drift.

3. Submission Guidelines and Important Dates

The submission website is located at: http://ees.elsevier.com/neucom/default.asp. Please select SI: LPCICD at the “Article Type” step in the submission process.

Authors should prepare their manuscript according to the \”Guide for Authors\” available from the online submission page of the Neurocomputing journal at https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. All the papers will be peer-reviewed following the Neurocomputing reviewing procedures.

Important Dates

Paper submission due: October 23, 2017.

First notification: January 23, 2018.

Revision: March 23, 2018.

Final decision: April 23, 2018.

Expected publication date: June 22, 2018 (tentative).

4. Guest Editors

Dr. Shuo Wang, University of Birmingham, UK (s.wang@cs.bham.ac.uk)

Dr. Leandro L. Minku, University of Leicester, UK (leandro.minku@leicester.ac.uk)

Prof. Nitesh Chawla, University of Notre Dame, U.S. (nchawla@nd.edu)

Prof. Xin Yao, The Southern University of Science and Technology, China (xiny@sustc.edu.cn)

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