Classification with costly features in hierarchical deep sets.
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| Title: | Classification with costly features in hierarchical deep sets. |
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| Authors: | Janisch, Jaromír1 (AUTHOR) jaromir.janisch@fel.cvut.cz, Pevný, Tomáš1 (AUTHOR), Lisý, Viliam1 (AUTHOR) |
| Source: | Machine Learning. Jul2024, Vol. 113 Issue 7, p4487-4522. 36p. |
| Subjects: | Deep reinforcement learning, XML (Extensible Markup Language), Classification, Reinforcement learning |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 08856125 |
| DOI: | 10.1007/s10994-024-06565-4 |
