Special Issue "Explainable Computational Intelligence, Theory, Methods and Applications"
Deadline for manuscript submissions: 31 December 2021.
Interests: statistical machine learning; risk modeling; multivariate statistical methods; time series analysis; statistical bioinformatics; wavelet statistics; biosignal analysis
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
Manuscript Submission Information
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- Dimension reduction
- Feature extraction
- Machine learning
- Explainable AI
- Explainable data analytics