Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs

• URL: http://arxiv.org/abs/2007.16010v1
• Date: Mon, 13 Jul 2020 07:50:53 GMT
• Title: Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs
• Authors: Subhadip Maji, Arijit Ghosh Chowdhury, Raghav Bali and Vamsi M Bhandaru
• Abstract summary: We introduce a model algorithm which calculates phrase-wise importance of input features. Our approach is robust to outliers, implying that it only captures the essential aspects of the input.
• Score: 3.6888633946892044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks in NLP have enabled systems to learn complex non-linear relationships. One of the major bottlenecks towards being able to use DNNs for real world applications is their characterization as black boxes. To solve this problem, we introduce a model agnostic algorithm which calculates phrase-wise importance of input features. We contend that our method is generalizable to a diverse set of tasks, by carrying out experiments for both Regression and Classification. We also observe that our approach is robust to outliers, implying that it only captures the essential aspects of the input.

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