RT Journal Article
T1 Error-Tolerant Computation for Voting Classifiers With Multiple Classes
A1 Liu, Shanshan
A1 Reviriego Vasallo, Pedro
A1 Montuschi, Paolo
A1 Lombardi, Fabrizio
AB In supervised learning, labeled data are provided as inputs and then learning is used to classify new observations. Error tolerance should be guaranteed for classifiers when they are employed in critical applications. A widely used type of classifiers is based on voting among instances (referred to as single voter classifiers) or multiple voters (referred to as ensemble classifiers). When the classifiers are implemented on a processor, Time-Based Modular Redundancy (TBMR) techniques are often used for protection due to the inflexibility of the hardware. In TBMR, any single error can be handled at the cost of additional computing either once for detection or twice for correction after detection; however, this technique increases the computation overhead by at least 100%. The Voting Margin (VM) scheme has recently been proposed to reduce the computation overhead of TBMR, but this scheme has only been utilized for k Nearest Neighbors ( k NNs) classifiers with two classes. In this paper, the VM scheme is extended to multiple classes, as well as other voting classifiers by exploiting the intrinsic robustness of the algorithms. k NNs (that is a single voter classifier) and Random Forest (RF) (that is an ensemble classifier) are considered to evaluate the proposed scheme. Using multiple datasets, the results show that the proposed scheme significantly reduces the computation overhead by more than 70% for k NNs with good classification accuracy and by more than 90% for RF in all cases. However, when extended to multiple classes, the VM scheme for k NNs is not efficient for some datasets. In this paper, a new protection scheme referred to as k + 1 NNs is presented as an alternative option to provide efficient protection in those scenarios. In the new scheme, the computation overhead can be further reduced at the cost of allowing a very low percentage of errors that can modify the classification outcome.
PB IEEE
SN 0018-9545
YR 2020
FD 2020-09-21
LK https://hdl.handle.net/10016/31890
UL https://hdl.handle.net/10016/31890
LA eng
NO This work was supported in part by the ACHILLESProject PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Economy and Competitivity, in part by the Department of Research and Innovation of Madrid Regional Authority, in part by the EMPATIA-CM Research Project (Reference Y2018/TCS-5046), and in part by NSF under Grants CCF-1953961 and 1812467
DS e-Archivo
RD 1 jul. 2024