Call for Papers

• Advancements in Generative vs. Discriminative Approaches, including the role of mixture models, Bayesian networks, and hidden Markov models in providing structured, interpretable class representations
• Regularization and Sparsity
• Uncertainty Quantification in XAI, including methods for assessing inferential confidence, stability, and reproducibility of model explanations
• Latent variable modelling
• Advances bridging Statistical and ML Models

Submissions should present high-quality contributions in these areas and advance the development of explainable modelling for complex data settings. 

Scope and Submission

This Special Issue is open for submissions, with full papers accepted from April 1, 2026 to October 31, 2026. All manuscripts submitted to this Special Issue will undergo the journal’s classical double-blind reviewing process. Papers should be submitted via the journal’s electronic submission system using the “Submit manuscript” option on the ADAC website.

Guest Editors

• Krzysztof Jajuga, Wrocław University of Economics and Business, Poland
• Francesca Greselin, University of Milano-Bicocca, Italy
• Salvatore Ingrassia, University of Catania, Italy
• Adalbert F.X. Wilhelm, Constructor University Bremen, Germany 

Call for Papers

Special Issue on “Advances in Mixture Models for Complex Data”
Submission deadline: November 30, 2026

We are pleased to announce a forthcoming Special Issue of the Journal of Classification dedicated to “Advances in Mixture Models for Complex Data.” This issue will showcase recent methodological developments and innovative applications in mixture modelling where the data exhibit structural or practical complexity.

Topics of interest include (but are not limited to):

• Mixture models for multivariate longitudinal data
• Cluster-weighted models
• Mixture approaches for compositional data
• Mixture models for binned data
• Mixture approaches for mixed-type data
• Mixture models for text data

Submissions should introduce novel methodology for mixture modelling in complex settings and demonstrate compelling substantive applications. Up-to-date review papers on state-of-the-art mixture approaches may also be considered (note that review articles in the journal are published relatively rarely).

Manuscripts that do not address mixture modelling for complex data will not be considered for this Special Issue.

Scope and Submission

The timing of this Special Issue is intended to facilitate submissions from participants of the 19th Conference of the International Federation of Classification Societies (IFCS 2026, Milan) and the MBC2 Meeting (Catania, August 2026). However, presentation at these meetings is not required for submission.

All submissions must:

• Present original, unpublished work not under consideration elsewhere
• Follow the author guidelines of the Journal of Classification
• Be submitted via the journal’s Editorial Manager system
• Select the submission type: “S.I.: Advances in Mixture Models for Complex Data”

All papers will undergo the standard peer-review process of the journal.

Guest Editors

• Antonio Punzo, University of Catania, Italy
• Andriëtte Bekker, University of Pretoria, South Africa
• Sanjeena Dang, Carleton University, Canada
• Volodymyr Melnykov, University of Alabama, USA

Further details and submission instructions are available on the journal’s official website.