Li, Yuchen
Xiong, Haoyi
Kong, Linghe
Wang, Shuaiqiang
Sun, Zeyi
Chen, Hongyang
Chen, Guihai
Yin, Dawei
Chapter History
First Online: 17 September 2023
Ethics
: <b>Ethical Statement.</b> The authors declare that they have listed all conflicts of interest. This article does not contain any studies with human participants or animals performed by any of the authors. All research and analysis presented in this paper will adhere to ethical principles of honesty, integrity, and respect for human dignity. Sources of information will be cited accurately and fully, and any potential conflicts of interest will be disclosed. Informed consent will be obtained from human subjects involved in the research, and any sensitive or confidential information will be handled with the utmost discretion. Data they used, the data processing and inference phases do not contain any user personal information. This work does not have the potential to be used for policing or the military. The rights and welfare of all individuals involved in this research project will be respected, and no harm or discomfort will be inflicted upon them. This paper strives to maintain high ethical standards and promote the advancement of knowledge in an ethical and responsible manner.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.