Call for Papers Special issue on “Granular Computing in Machine Learning” Guest Editors: Degang Chen, Weihua Xu, and Jinhai Li
Purpose: Granular computing (GrC), originally proposed by Zadeh in 1979, plays a fundamental role in human reasoning and problem solving. The three basic issues in the theory of GrC are information granulation, organization and causation. Specifically, the information granulation involves the decomposition of a whole into parts, the organization involves integration of parts into a whole, and the causation involves the association of causes with effects. They have been applied in many fields such as machine learning, data mining and knowledge discovery. Machine learning is programming computers to optimize a performance criterion using example data or past experience. Based on what kind of strategy we use in learning, it can be classified into rote learning, learning from instruction, learning by deduction, learning by analogy, explanation-based learning, and learning from induction. In fact, no matter what kind of learning we adopt in solving a real problem, the higher the learning accuracy or efficiency is, the better the learning result of a classifier is. In recent years, we have witnessed a rapidly growing interest in GrC viewed as a new asset of studies of machine learning. Especially, for some certain types of machine learning problems, we indeed observed that data pre-processing via information granulation or combining GrC technique with machine learning method can improve learning accuracy or efficiency, such as GrC based feature selection, GrC based classification algorithm, concept-cognitive learning with GrC, cost-sensitive active learning via GrC, multi-granulation learning in cognition, etc. To the best of our knowledge, the studies on machine learning problems in big data environment with the aid of GrC are very limited. It is time to establish new theories, methodologies and tools of GrC in machine learning. As an important fundamental theory in GrC, the principle of optimal information granulation from a practical machine learning problem often serves as a key evidence for facilitating the best formation of information granules. Meanwhile, some design scenarios even support the best formation of information granules in big data for improving learning accuracy or efficiency. This special issue aims to offer a systematic overview of this new research field and provide innovative approaches, models and systems to effectively support GrC based machine learning in big data. It is anticipated that this special issue will help foster future studies on the combination of GrC and machine learning
towards forming their own theories and models for dealing with big data.
Scope and Topics: The main topics of this special issue include, but are not limited to, the following: 1. New theories and methods of GrC 2. Concept-cognitive learning with multi-granularity 3. Clustering based on GrC 4. Multi-granulation learning in cognition 5. Feature selection via GrC 6. GrC based machine learning methods for big data 7. GrC and association rule mining 8. Optimal granule information transformation in dimension conversion 9. Combination of GrC technique with deep learning and its application 10. GrC via multi-label learning 11. Incremental learning based on synthesis and decomposition of information granules 12. Cost-sensitive active learning via GrC 13. Cognitive learning with tri-partition techniques (e.g., three-way decisions, shadowed sets, orthopairs)
Time line of special issue: Please submit a full-length paper through the Granular Computing journal online submission system and indicate it is to this special issue. Papers should be formatted by following GrC manuscript formatting guidelines. The submission procedure will be managed by the Guest Editors and strictly follow the rules of Granular Computing. The proposed key dates are following: Deadline of submission: March 31, 2018 1st round of review – comments to authors: June 30, 2018 Revision deadline: August 31, 2018 Submission of final version: October 31, 2018 For information about the journal of Granular https://www.editorialmanager.com/grco/default.aspx
Computing,
If you have any questions or suggestions, please do not hesitate to contact us: Degang Chen (North China Electric Power University, China) Email:
[email protected] Weihua Xu (Chongqing University of Technology, China) Email:
[email protected] Jinhai Li (Kunming University of Science and Technology, China) Email:
[email protected]
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