Devos, Laurens http://orcid.org/0000-0002-1549-749X
Perini, Lorenzo http://orcid.org/0000-0002-5929-9727
Meert, Wannes http://orcid.org/0000-0001-9560-3872
Davis, Jesse http://orcid.org/0000-0002-3748-9263
Chapter History
First Online: 18 September 2023
Ethical Statement
: Machine learning is widely used in many different application areas. With the wide adoption, machine learned models, including tree ensembles, increasingly become high-stake targets for attackers who might employ evasion attacks to achieve their goal. This work proposes a defense method against evasion attacks for tree ensembles. Together with other approaches like robust tree ensembles, this work is a step forward in our ability to protect against evasion attacks. This could further improve the trust in machine learning, and further accelerate its adoption, especially in sensitive application areas.Improved defenses will likely also result in the development of improved counter-attacks. We strongly feel that it is in the interest of the research community that (1) the research community stays on top of these developments so that machine learning libraries can adapt if necessary, and (2) all work done in this area is open-access. For that reason, all resources and codes are publicly available at ExternalRef removed.
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.