An enhanced binary classifier for Edge devices

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Internet of Things, Edge Computing and 5G networks are rapidly becoming key enablers for a wide range of new services. This new infrastructure opens more possibilities for devices with embedded systems and microcontrollers to perform data processing and analysis. The need for real-time decisions now defeats the purpose of sending sensor data to the Cloud for further processing. Storing and analyzing data near its source introduce new constraints on resource-constrained and battery-powered devices. Scalable, Efficient, and Fast classifieR (SEFR) is one such algorithm that brings machine learning to low-power microcontrollers. The main contribution of this work is the definition of a new decision boundary, also known as the bias, to improve the accuracy of the SEFR binary classifier. Experiments performed on an 8-bit Arduino microcontroller using 5-fold cross-validation demonstrate that the improved algorithm (iSEFR) increases Precision, Recall and Accuracy by an average of 9%, 14% and 11% respectively. F1-score increases on average from 80% to 92%, which represents an increase of 12% in the model accuracy for even class distribution. MCC also increases from 64% to 85% by an average of 21%, and this represents a better binary classification quality. Training time and space complexity remain constant but the testing time increases marginally by an average of 0.0013 seconds for low-capacity processors. Moreover, iSEFR performs better on a 64-bit intel i5 processor with thousands of data points and features. Experiments also demonstrate that the accuracy of iSEFR is comparable to Support Vector Machine with a linear kernel.