Khawaja Moyeezullah Ghori

TL;DR: The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%) and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing.

TL;DR: It is concluded that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers, which has opened a new area of research in NTL detection.

TL;DR: This work uses three classifiers: random forest, K -nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records and computes 14 performance evaluation metrics across these classifiers to provide insights into deciding which classifier can be more useful under given scenarios for NTL detection.

TL;DR: The Incremental Feature Selection (IFS) algorithm is proposed which first uses feature importance to identify the most relevant features for NTL detection and then these features are used to test three classifiers namely CatBoost, Decision Tree (DT) Classifier and K-Nearest Neighbors (KNN), which have brought down the overall computation time of the classifiers.

TL;DR: In this paper, a range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat non-technical loss (NTL) and the results are compared with the untreated imbalanced dataset.