Assessing the quality of the datasets by identifying mislabeled samples

Vaibhav, Pulastya,; Gaurav, Nuti,; Atri, Yash Kumar; Chakraborty, Tanmoy

doi:10.1145/3487351.3488361

Cited by 2 publications

(1 citation statement)

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“…The task of detecting, eliminating, or correcting mislabeled samples in training datasets is critical in machine learning, with methodologies broadly classified into three categories [253], [107], [254], [103], [255], [256], [31], [15]. Firstly, visual and interactive analytics are prominent and utilized to identify mislabeled data points by detecting outliers in low-dimensional representations [103], [104].…”

Section: Mislabel Detectionmentioning

confidence: 99%

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Yong¹

2021

Explainable Artificial Intelligence for Smart Cities

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Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on algorithmic and model-centric perspectives, this work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI). We categorize existing work into three categories subject to their purposes: interpretations of deep models, referring to feature attributions and reasoning processes that correlate data points with model outputs; influences of training data, examining the impact of training data nuances, such as data valuation and sample anomalies, on decision-making processes; and insights of domain knowledge, discovering latent patterns and fostering new knowledge from data and models to advance social values and scientific discovery. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors. In this way, our study offers a comprehensive, data-centric examination of XAI from a lens of data mining methods and applications.

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