# An Active Learning Framework with a Class Balancing Strategy for Time Series Classification

arXiv: https://arxiv.org/abs/2405.12122 · HTML: https://mlsanity.com/paper/2405.12122/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 113 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning](https://mlsanity.com/paper/2104.00543/) (2021)
- [Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations](https://mlsanity.com/paper/2307.07893/) (2023)
- [Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training](https://mlsanity.com/paper/2107.05458/) (2021)

## Abstract (arXiv)

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.