  Training machine learning models for classification tasks often requires
labeling numerous samples, which is costly and time-consuming, especially in
time series analysis. This research investigates Active Learning (AL)
strategies to reduce the amount of labeled data needed for effective time
series classification. Traditional AL techniques cannot control the selection
of instances per class for labeling, leading to potential bias in
classification performance and instance selection, particularly in imbalanced
time series datasets. To address this, we propose a novel class-balancing
instance selection algorithm integrated with standard AL strategies. Our
approach aims to select more instances from classes with fewer labeled
examples, thereby addressing imbalance in time series datasets. We demonstrate
the effectiveness of our AL framework in selecting informative data samples for
two distinct domains of tactile texture recognition and industrial fault
detection. In robotics, our method achieves high-performance texture
categorization while significantly reducing labeled training data requirements
to 70%. We also evaluate the impact of different sliding window time intervals
on robotic texture classification using AL strategies. In synthetic fiber
manufacturing, we adapt AL techniques to address the challenge of fault
classification, aiming to minimize data annotation cost and time for
industries. We also address real-life class imbalances in the multiclass
industrial anomalous dataset using our class-balancing instance algorithm
integrated with AL strategies. Overall, this thesis highlights the potential of
our AL framework across these two distinct domains.
