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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.06685 |
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| _version_ | 1866909454523432960 |
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| author | Padala, Manisha Nagalapatti, Lokesh Tyagi, Atharv Narayanam, Ramasuri Saini, Shiv Kumar |
| author_facet | Padala, Manisha Nagalapatti, Lokesh Tyagi, Atharv Narayanam, Ramasuri Saini, Shiv Kumar |
| contents | We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous attributes and the corresponding subsets of records that serve as evidence to the user. The process of identifying these insight blocks is challenging due to (i) the absence of labeled anomalies, (ii) the exponential size of the subset search space, and (iii) the complex dependencies among attributes, which obscure the true sources of anomalies. Simple frequency-based methods fail to capture these dependencies, leading to inaccurate results. To address this, we introduce Tab-Shapley, a cooperative game theory based framework that uses Shapley values to quantify the contribution of each attribute to the data's anomalous nature. While calculating Shapley values typically requires exponential time, we show that our game admits a closed-form solution, making the computation efficient. We validate the effectiveness of our approach through empirical analysis on real-world tabular datasets with ground-truth anomaly labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tab-Shapley: Identifying Top-k Tabular Data Quality Insights Padala, Manisha Nagalapatti, Lokesh Tyagi, Atharv Narayanam, Ramasuri Saini, Shiv Kumar Machine Learning We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous attributes and the corresponding subsets of records that serve as evidence to the user. The process of identifying these insight blocks is challenging due to (i) the absence of labeled anomalies, (ii) the exponential size of the subset search space, and (iii) the complex dependencies among attributes, which obscure the true sources of anomalies. Simple frequency-based methods fail to capture these dependencies, leading to inaccurate results. To address this, we introduce Tab-Shapley, a cooperative game theory based framework that uses Shapley values to quantify the contribution of each attribute to the data's anomalous nature. While calculating Shapley values typically requires exponential time, we show that our game admits a closed-form solution, making the computation efficient. We validate the effectiveness of our approach through empirical analysis on real-world tabular datasets with ground-truth anomaly labels. |
| title | Tab-Shapley: Identifying Top-k Tabular Data Quality Insights |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.06685 |