Skip to main content


International Journal of Machine Learning and Cybernetics

International Journal of Machine Learning and Cybernetics OnlineFirst articles

13-10-2021 | Original Article

Multi-label space reshape for semantic-rich label-specific features learning

Existing label-specific features learning techniques mainly use embedding-based researching methods. However, there exist many problems such as inadequate consideration of label semantics, the sparseness of selected features and so on. Herein, the …

13-10-2021 | Original Article Open Access

Building heterogeneous ensembles by pooling homogeneous ensembles

Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In …

11-10-2021 | Original Article

Conflict analysis based on three-way decision for trapezoidal fuzzy information systems

Three-way decision is a decision-making model in line with people’s cognition and aims to think and deal with problems at three levels or three aspects. One of the main purposes of conflict analysis is to partition the set of agents into three …

10-10-2021 | Original Article

Towards unified on-road object detection and depth estimation from a single image

On-road object detection based on convolutional neural network (CNN) is an important problem in the field of automatic driving. However, traditional 2D object detection aims to accomplish object classification and location in image space, lacking …

09-10-2021 | Original Article

Semi-supervised label enhancement via structured semantic extraction

Label enhancement (LE) is a process of recovering the label distribution from logical labels in the datasets, the goal of which is to better express the label ambiguity through the form of label distribution. Existing LE work mainly focus on …

Current Publications

About this journal

Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.

The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.

Key research areas to be covered by the journal include:

  • Machine Learning for modeling interactions between systems
  • Pattern Recognition technology to support discovery of system-environment interaction
  • Control of system-environment interactions
  • Biochemical interaction in biological and biologically-inspired systems
  • Learning for improvement of communication schemes between systems
Additional information