Abstract
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 language | English |
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Title of host publication | 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) |
Pages | 361-368 |
DOIs | |
Publication status | Published - 2015 |
Event | 27th IEEE International Conference on Tools with Artificial Intelligence - Vietri sul Mare, Italy Duration: 9 Nov 2015 → 11 Nov 2015 |
Conference
Conference | 27th IEEE International Conference on Tools with Artificial Intelligence |
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Abbreviated title | ICTAI 2015 |
Country/Territory | Italy |
City | Vietri sul Mare |
Period | 9/11/15 → 11/11/15 |