Special Issue "Explainable Computational Intelligence, Theory, Methods and Applications"

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Dr. Shengkun Xie
Website SciProfiles
Guest Editor
Ted Rogers School of Management, Ryerson University, Toronto, Ontario, Canada
Interests: statistical machine learning; risk modeling; multivariate statistical methods; time series analysis; statistical bioinformatics; wavelet statistics; biosignal analysis

Special Issue Information

Dear Colleagues,

Explainable AI, explainable data analysis, and explainable data analytics are now playing an important role in machine learning and artificial intelligence. This is because many machine learning techniques are highly technical, and the models involved are complicated so that it is not easy to understand how the input data are processed. When data visualization or understanding of the key features extracted using complicated statistical and mathematical approaches are crucial to real-world applications, improving data interpretability becomes necessary and essential. For example, the visualization of low dimensional extracted features is typically crucial in computer-aided medical diagnosis. Further, the study of high dimensional data for business decision making is rapidly growing since it often leads to more accurate information so that the decision is more reliable than others. To better understand the natural variation and pattern, attempts to improve the data interpretability have been an ongoing challenging problem, mainly in the area of complex statistical data analysis.

Recently, research on explainable computational intelligence has gained much attention in many fields of study, including engineering, science, and social science. Further, in machine learning, novel dimension reduction and feature extraction methods are particularly needed to facilitate data classification or clustering, depending on the availability of data labels. This Special Issue aims at promoting advanced mathematical, statistical, and computational techniques, which help to improve explainable data analysis or understanding the models that we consider. The techniques include but are not limited to:

  • Sparse statistical methods;
  • Feature extraction and data fusion;
  • Explainable artificial neural networks;
  • Data dimension reduction;
  • Functional data analysis;
  • Time–frequency domain approaches.

Both theoretical development and applied work, including application and methodological development, are welcome.

Dr. Shengkun Xie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Dimension reduction
  • Feature extraction
  • Sparsity
  • Machine learning
  • Explainable AI
  • Explainable data analytics

Published Papers (1 paper)

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Open AccessArticle
Modelling Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway via Artificial Neural Networks
Computation 2020, 8(3), 64; https://doi.org/10.3390/computation8030064 - 08 Jul 2020
A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It [...] Read more.
A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models. Full article
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