2019 IEEE Symposium Series on Computational Intelligence


IEEE Symposium Series on Computational Intelligence

December 6-9, 2019 Xiamen, China

 

IEEE Symposium on Explainable Data Analytics in Computational Intelligence (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.

Topics

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

Jian Wang, China University of Petroleum (East China), China

Email: wangjiannl@upc.edu.cn

Homepage

Yifei Pu

Yifei Pu, Sichuan University, China

Email: puyifei@scu.edu.cn

Homepage

Yinan Guo

Yinan Guo, China University of Mining and Technology, China

Email: guoyinan@cumt.edu.cn

Homepage

Program Committee

Chao Zhang Dalian University of Technology, China chao.zhang@dlut.edu.cn
Chunlei Wu China University of Petroleum (East China), China wuchunlei@upc.edu.cn
Dongpo Xu Northeast Normal University, China dongpoxu@gmail.com
Gaige Wang Ocean University of China, China gaigewang@gmail.com
Haibo Bao Southwest University, China hbbao@swu.edu.cn
He Huang Soochow University, China huang@suda.edu.cn
Hongmei Shao China University of Petroleum (East China), China hmshao@upc.edu.cn
Hua Chun Inner Mongolia University for Nationalities, China chunhua99018074@163.com
Huisheng Zhang Dalian Maritime University, China zhhuisheng@163.com
Jianxun Zhang Chongqing University of Technology, China zjx@cqut.edu.cn
Jie Yang Dalian University of Technology, China yangjiee@dlut.edu.cn
Jin Hu Chongqing Jiaotong University, China jhu@cqjtu.edu.cn
Junqing Li Shandong Normal University, China lijunqing@lcu-cs.com
Kaustuv Nag Jadavpur University, India kaustuv.nag@gmail.com
Leiquan Wang China University of Petroleum (East China), China wangleiquan@upc.edu.cn
Lijun Liu Dalian Minzu University, China manopt@163.com
Long Li Hengyang Normal University, China long_li1982@163.com
Lu Wu National Supercomputer Center in Jinan, China wul@sdas.org
Mingwen Shao China University of Petroleum (East China), China mwshao@upc.edu.cn
Peng Ren China University of Petroleum (East China), China pengren@upc.edu.cn
Qinwei Fan Xi'an Polytechnic University, China qinweifan@126.com
Tian Tian Shandong Jianzhu University, China tiantian@sdjzu.com
Tolga Ensari Istanbul University, Turkey ensari@istanbul.edu.tr
Weishan Zhang China University of Petroleum (East China), China zhangws@upc.edu.cn
Weifeng Liu China University of Petroleum (East China), China liuwf@upc.edu.cn
Xiaoshuai Ding Southeast University, China missdxss@163.com
Xu Yu Qingdao University of Science and Technology, China yuxu0532@163.com
Yan Liu Dalian Polytechnic University, China liuyan@dlpu.edu.cn
Yanjiang Wang China University of Petroleum (East China), China yjwang@upc.edu.cn
Yanpeng Qu Dalian Maritime University, China yanpengqu@dlmu.edu.cn
Yuming Feng Chongqing Three Gorges University, China yumingfeng25928@163.com
Yuyan Han Liaocheng University, China hanyuyan@lc-cs.com
Zhanquan Sun University of Shanghai for Science and Technology, China sunzhq@usst.edu.cn