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: Explaining the decision-making process of "black-box" Deep Learning (DL) models used in text classification , particularly within the biomedical domain. 113941
Post-hoc explanation of black-box classifiers using confident itemsets Are you researching for a specific industry
: These models often require large datasets and can be sensitive to "adversarial noise" (small character-level changes that fool the AI). The identifier refers to a specific research article
The identifier refers to a specific research article titled "Post-hoc explanation of black-box classifiers using confident itemsets" , published in the journal Expert Systems with Applications (Volume 165, March 2021). Key Details of the Research Authors : Milad Moradi and Matthias Samwald.
: It addresses the "black-box" problem where complex neural networks provide accurate results but lack transparency, which is critical for high-stakes fields like healthcare. Understanding "Deep Text"
: Sentiment analysis of customer reviews, biomedical literature summarization, and disease-treatment classification.
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