Robust bag classification approach for multi-instance learning via subspace fuzzy clustering

Multi-instance learning (MIL) allows predictive algorithms to use complex data representation. The data in MIL is organized in the form of labeled bags of instances, and the labels of instances are not available in the training phase. The processing and classification of complex bag representation result in a complicated hypothesis space. Moreover, identifying essential instances inside is also important, as these instances trigger positive labels for the bag and play a vital role in the model interpretation and bag classification. The recent MIL algorithms are not robust to tackle hypothesis space complication. Additionally, the existing instance selection algorithms are based on explicit assumptions regarding the relationship of instances to the bag label. However, these assumptions may hold for a specific bag in the dataset but may not apply to the whole dataset. To deal with the hypothesis space complication and robust instance selection without any prior assumption, this paper proposes a fuzzy subspace clustering approach for robust instance selection and ensemble-based variant for Fisher vector (FV) encoding to solve MIL problems, named (FCBE-miFV). Specifically, the proposed algorithm uses a subspace fuzzy clustering approach to compute instance selection probabilities, selects essential instances from the bag, transforms the input bag into FV, and classifies the generated bag encodings using a stacking-based ensemble approach to obtain improved bag level classification performance. The proposed FCBE-miFV improves model performance by incorporating essential instances in the bag encoding process. The experimental results show that the FCBE-miFV obtained comparable performance to the state-of-the-art MIL problems.

Automated summary

Multi-instance learning (MIL) allows predictive algorithms to use complex data representation. This paper proposes a fuzzy subspace clustering approach and an ensemble-based variant of Fisher vector (FV) encoding, named FCBE-miFV, to tackle the challenges in MIL, such as hypothesis space complication and robust instance selection. The proposed algorithm improves model performance by incorporating essential instances in the bag encoding process.