Probabilistic personalised cascade with abstention

Shpakova, Tatiana; Sokolovska, Nataliya

doi:10.1016/j.patrec.2021.03.029

Pattern Recognition Letters

2021

DOI: 10.1016/j.patrec.2021.03.029

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Probabilistic personalised cascade with abstention

Tatiana Shpakova

Nataliya Sokolovska

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2024

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Cited by 2 publications

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Machine learning with a reject option: a survey

Hendrickx,

Perini,

Van der Plas

et al. 2024

Mach Learn

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Machine learning with a reject option: a survey

Hendrickx,

Perini,

Van der Plas

et al. 2024

Mach Learn

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Classification with costly features in hierarchical deep sets

Janisch,

Pevný,

Lisý

2024

Mach Learn

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Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features’ cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service.

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Probabilistic personalised cascade with abstention

Cited by 2 publications

References 10 publications

Machine learning with a reject option: a survey

Machine learning with a reject option: a survey

Classification with costly features in hierarchical deep sets

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