An adaptive binary classifier for highly imbalanced datasets on the Edge

Edge machine learning brings intelligence to low-power devices at the periphery of a network. By running machine learning algorithms on the Edge, classification can be performed faster without the need to transmit large data volumes across a network. However, on-device training is often not feasible since Edge devices have limited computing and storage resources. Improved, Scalable, Efficient, and Fast classifieR (iSEFR) is a classifier that performs both training and testing on low-power devices using linearly separable balanced datasets. The novelty of this work is the improvement of the iSEFR accuracy by fine-tuning the algorithm with datasets having an uneven class distribution. Three adaptive linear function transformation techniques were proposed to improve the decision threshold which is in the form of a linear function. Experiments using stratified sampling with 5-fold cross-validation demonstrate that one of the proposed techniques significantly improved F1-score, Recall and Matthews Correlation Coefficient (MCC) by an average of 23 %, 35 % and 21 % compared to iSEFR. Further evaluation of this technique in a Fog environment using highly imbalanced datasets such as credit card fraud, network intrusion and diabetic retinopathy also showed a significant increase of 38 %, 44 % and 30 % in F1-score, Recall and MCC with a Precision of 97 %. The adaptive binary classifier maintained the time complexity of iSEFR without altering the class imbalance.