IEEE Symposium on Explainable Data Analytics in Computational Intelligence (IEEE EDACI)

The latest advances in Machine Learning are reaching critical areas such as medicine, criminal justice systems, financial markets and many other real applications. From a mathematical point of view, the search of the model focuses on the minimization of a cost function or the maximization of a likelihood function. Thus, the performance of the model is measured almost exclusively on the results we can get according to some rightly chosen metrics. This tendency has led to more and more sophisticated algorithms to the cost of explainability (Interpretability). Having an accurate model is good, but explanations lead to better products. There is an increasing concern about the acceptance of the computation intelligent learning models, where the explainability is the key measures for evaluating models. 

In recent years, the advancements in computational intelligence have allowed researchers to tackle data driven problems with explainability and integrate efficient optimization algorithms for solving them. Due to the long-term memory, nonlocality, and weak singularity fractional differential operator, there is an increasing applications on fractional calculus based computation intelligent models. It is also an interesting research area to pay more attention on the interpretability aspect. From Explainable Data Analytics concern, this symposium aims to highlight the latest results from world leading research labs, academia and industry in the fields of Computational Intelligence, whose issues include corresponding efficient neural network methods, evolutionary algorithms and Neuro-fuzzy optimization techniques. In order to encourage research interactions, we welcome submissions describing innovative operations research methods that are able to provide state-of-the-art solutions to the above mentioned issues as well. Researches incorporating real-world applications are also highly encouraged.


The symposium will cover all the issues, researches and developments of the state-of-the-art EDACI-based learning models in solving various problems. CI application areas include, but are not limited to:

  • Neural Network Learning Models
  • Feature Analysis based Neural Networks Models
  • Dimensionality reduction and analysis of large and complex data
  • Fractional Evolutionary Optimization Computation
  • Feature Analysis based Evolutionary Computation Algorithms
  • Feature Analysis based Neuro-fuzzy Systems
  • Extracting Understanding from Large-scale Data Resources
  • Feature learning and feature engineering
  • Time Series and System Modeling
  • Flexible Neuro-fuzzy Systems
  • Interpretability of Fuzzy Rule-based Systems for Nonlinear Modeling
  • Dimensionality Reduction and Analysis of Large and Complex Dataset
  • Neural Networks, Fuzzy and Evolutionary based Explainable Control Systems 
  • Optimization of big data in complex systems
  • Expert and Decision Support Systems
  • System Identification and Learning

Symposium Chairs

Jian Wang
China University of Petroleum (East China), China
Huaqing Li
Southwest University, China
Kai Zhang
China University of Petroleum (East China), China

Programme Committee

Chao Zhang, Dalian University of Technology, China
Chunlei Wu, China University of Petroleum (East China), China
Dongpo Xu, Northeast Normal University, China
Gaige Wang, Ocean University of China, China
Haibo Bao, Southwest University, China
He Huang, Soochow University, China
Hongmei Shao, China University of Petroleum (East China), China
Hua Chun, Inner Mongolia University for Nationalities, China,
Huisheng Zhang, Dalian Maritime University, China
Jianxun Zhang, Chongqing University of Technology, China
Jie Yang, Dalian University of Technology, China
Jin Hu,Chongqing Jiaotong University, China
Junqing Li, Shandong Normal University, China
Kai Zhang, China University of Petroleum (East China), China
Kaustuv Nag, Jadavpur University, India
Leiquan Wang, China University of Petroleum (East China), China
Lijun Liu, Dalian Minzu University, China
Long Li, Hengyang Normal University, China
Lu Wu, National Supercomputer Center in Jinan, China
Mingwen Shao,China University of Petroleum (East China), China
Peng Ren, China University of Petroleum (East China), China
Qinwei Fan, Xi’an Polytechnic University, China
Shibao Li, China University of Petroleum (East China), China
Tian Tian, Shandong Jianzhu University, China
Tolga Ensari, Istanbul University, Turkey
Weishan Zhang, China University of Petroleum (East China), China
Weifeng Liu, China University of Petroleum (East China), China
Xiaoshuai Ding, Southeast University, China
Xu Yu, Qingdao University of Science and Technology, China
Yan Liu, Dalian Polytechnic University, China
Yanjiang Wang, China University of Petroleum (East China), China
Yanpeng Qu, Dalian Maritime University, China
Yuming Feng, Chongqing Three Gorges University, China
Yuyan Han, Liaocheng University, China
Zhanquan Sun, University of Shanghai for Science and Technology, China