ECE544NA Final Project: Robust Machine Learning Hardware via Classifier Ensemble
نویسنده
چکیده
In this paper, we propose to use classifier ensemble (CE) as a method to enhance the robustness of machine learning (ML) kernels in presence of hardware error. Different ensemble methods (Bagging and Adaboost) are explored with decision tree (C4.5) and artificial neural network (ANN) as base classifiers. Simulation results show that ANN is inherently tolerant to hardware errors with up to 10% hardware error rate. With simple majority voting scheme, CE is able to effectively reduce the classification error rate for almost all tested data sets, with maximum test error reduction of 48%. For tree ensemble, Adaboost with decision stump as weak learner gives best results; while for ANN, bagging and boosting outperform each other depending on data set.
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