Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Recent Progress in Autonomous Machine Learning

Recent Progress in Autonomous Machine Learning

Journal
Impact Score 14.36

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 10-12-2022
Journal Impact Score: 14.36
Journal Name: Information Sciences
Publisher: Information Sciences

Special Issue Call for Papers


Autonomous Machine Learning (AML) refers to a learning system having flexible characteristic to evolve both its network structure and parameters on the fly. It is capable of initiating its learning process from scratch with/without a predefined network structure while its knowledge base is automatically constructed in real-time. AML is built upon two fundamental principles: one-pass learning strategy and self-evolving network structure. The former one reflects a situation where a data point is directly discarded once learned to assure bounded memory and computational burdens while the latter lies in the self-reconfiguration aptitude of AML where its network size can increase or reduce in respect to varying data distributions. AMLs have been proven to be useful in handling real-time data streams where a learning system confronts never-ending information flow which does not follow static or predictable data distributions rather drifting overtime with different types, magnitudes and types. Variants of AMLs are capable of quickly reacting to those drifting distributions regardless of how slow, fast, sudden, gradual, cyclic changing distributions might be while retaining computationally light characteristics. In addition, the AMLs have grown into various application domains not only limited to regression, classification, clustering but also control and reinforcement learning. In a nutshell, it is
enabled by the fact that AMLs aim to balance between stability and plasticity of a learning system.



Recent challenges in machine learning renders innovation of AMLs urgently needed. The advent of deep learning technologies is a concrete example. Existing DNNs mostly rely on a static and offline learning principle limiting its feasibility in the streaming environments. On the other hand, DNNs are well-known for its feature learning power being able to handle unstructured problems with large input dimension and target classes. The network structure of DNNs are difficult to evolve because of the absence of local and spatial contexts. The multi-layer nature of DNNs complicate the self-evolving strategy. Insertion of a new layer definitely leads to the catastrophic forgetting problem. Another research opportunity of AMLs is identified in the context of lifelong/continual learning where the goal is not only to adapt to changing environments but also to actualize a lifelong learning agent with knowledge retention property. That is, a learning agent must not suffer from the catastrophic forgetting problem when adapting to a new context. The fact that AMLs are normally designed in the local learning environment should be useful for this purpose. Only relevant knowledge is stimulated by new tasks while others remain silent. The application of AMLs in the transfer learning domain deserves in-depth study. Unlike traditional AML involving only a single stream, the case of multi-streams remains an open issue. The main goal of this problem is to create a domain-invariant network handling both source stream and target stream equally well. The challenge of this topic is evident in the covariate shift problem between source stream and target stream. As with the single stream case, the concept drift occurs here in each stream in different time periods.



This special issue aims to bring together recent research works of AMLs. Particular interest lies in the integration of AMLs in handling advanced issues of machine learning as abovementioned. We solicit original works that have not been published nor under consideration in other publication venues.



Topic of Interest
The topic of interest includes the following but not limited to




Important Dates



Manuscript Submission Deadline: July 1st, 2021
First Round of Reviews: September 30th, 2021
Revised Paper Submission: December 10th, 2022
Second Round of Reviews: February 1st, 2022
Expected Publication Date: May 1st, 2022



Submission Instruction



To be considered in this special issue, author should select VSI: Recent Progress in AML in the Elsevier editorial system.



Guest Editors
Mahardhika Pratama
email: [email protected]
School of Computer Science and Engineering
Nanyang Technological University
Singapore
 



Edwin Lughofer



email: [email protected]
Department of Knowledge-Based Mathematical Systems
Johannes Keppler University
Austria
 



Plamen P. Angelov



email: [email protected]



School of Computing and Communications
Lancaster University
UK

Other Special Issues on this journal

Publisher
Journal Details
Closing date
G2R Score
Recent Progress in Autonomous Machine Learning

Recent Progress in Autonomous Machine Learning

Information Sciences
Closing date: 10-12-2022 G2R Score: 14.36

Closed Special Issues

Publisher
Journal Details
Closing date
G2R Score
Membrane Computing

Membrane Computing

Information Sciences
Closing date: 31-05-2021 G2R Score: 14.36
Secure and Smart Autonomous Multi-Robot Systems

Secure and Smart Autonomous Multi-Robot Systems

Information Sciences
Closing date: 01-02-2021 G2R Score: 14.36
Robust Recognition Systems against Adversarial Attacks

Robust Recognition Systems against Adversarial Attacks

Information Sciences
Closing date: 15-07-2020 G2R Score: 14.36
Advances in Industrial Artificial Intelligence (AIAI)

Advances in Industrial Artificial Intelligence (AIAI)

Information Sciences
Closing date: 30-09-2019 G2R Score: 14.36
Privacy Computing: Principles and Applications

Privacy Computing: Principles and Applications

Information Sciences
Closing date: 30-05-2018 G2R Score: 14.36
Advanced Methods for Evolutionary Many Objective Optimization

Advanced Methods for Evolutionary Many Objective Optimization

Information Sciences
Closing date: 01-02-2018 G2R Score: 14.36
Distributed Event-Triggered Control and Estimation in Resource-Constrained Cooperative Networks

Distributed Event-Triggered Control and Estimation in Resource-Constrained Cooperative Networks

Information Sciences
Closing date: 15-01-2018 G2R Score: 14.36
Business Analytics – Emerging Trends and Challenges

Business Analytics – Emerging Trends and Challenges

Information Sciences
Closing date: 01-12-2017 G2R Score: 14.36
New energy-optimization challenges in the next generation Internet ecosystem

New energy-optimization challenges in the next generation Internet ecosystem

Information Sciences
Closing date: 01-12-2017 G2R Score: 14.36
Parallel and Distributed Data Mining

Parallel and Distributed Data Mining

Information Sciences
Closing date: 01-12-2017 G2R Score: 14.36
Granular Computing, Shadowed Sets, and Three-Way Decisions

Granular Computing, Shadowed Sets, and Three-Way Decisions

Information Sciences
Closing date: 15-10-2017 G2R Score: 14.36
Digital Manifolds in Computer Modeling

Digital Manifolds in Computer Modeling

Information Sciences
Closing date: 15-10-2017 G2R Score: 14.36
Innovative Smart Methods for Security: Emerging Trends and Research Challenges

Innovative Smart Methods for Security: Emerging Trends and Research Challenges

Information Sciences
Closing date: 17-08-2017 G2R Score: 14.36