Abstract
This paper proposes an Active Learning algorithm that could detect heart attacks based on different body measures, which requires much less data than the passive learning counterpart while maintaining similar accuracy. To that end, different parameters were tested, namely the batch size and the query strategy used. The initial tests on batch size consisted of varying its value until 50. From these experiments, the conclusion was that the best results were obtained with lower values, which led to the second set of experiments, varying the batch size between 1 and 5 to understand in which value the accuracy was higher. Four query strategies were tested: random sampling, least confident sampling, margin sampling and entropy sampling. The results of each approach were similar, reducing by 57% to 60% the amount of data required to obtain the same results of the passive learning approach.
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- 1.
Active Learning is also used in the ML branch of Reinforcement Learning; in this paper, we are confined to the ML branch of Supervised and Semi-Supervised Learning.
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This work is funded by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit - UIDB/00326/2020 or project code UIDP/00326/2020.
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Faria, B., Perdigão, D., Brás, J., Macedo, L. (2022). The Joint Role of Batch Size and Query Strategy in Active Learning-Based Prediction - A Case Study in the Heart Attack Domain. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_38
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