Conformal ECOC Machines

Firat Ismailoglu*, Evgueni Smirnov, Ralf Peeters

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Conformal prediction (CP) has received a significant attention in the last decade due to its capability of providing confidences for class predictions. However, the literature lacks of a computationally efficient implementation of CP for multi-class reduction techniques such as one-versus-all, ECOC etc. To address this issue we propose two implementations of CP for multi-class reduction: Conformal ECOC Machine (cECOC) and Conformal Poisson ECOC Machine (cpECOC). We show that both machines are computationally efficient and are capable of working with any reduction techniques based on binary code matrices. Moreover, we show that the second machine, cpECOC, probabilistically incorporates the error correction property of ECOC into CP framework using the Poisson binomial distribution. Conducted experiments on UCI datasets demonstrate that both machines output valid and efficient prediction sets.
Original languageEnglish
Title of host publication2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)
Publication statusPublished - 2015
Event27th IEEE International Conference on Tools with Artificial Intelligence - Vietri sul Mare, Italy
Duration: 9 Nov 201511 Nov 2015


Conference27th IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2015
CityVietri sul Mare


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