Machine Learning Training Data Redundancy

Machine Learning Training Data Redundancy: A Hidden Enemy of Model Accuracy

Machine learning has revolutionized the way we approach complex problems in various fields, from finance to healthcare. However, a crucial aspect of machine learning, the quality of the training data, is often overlooked. **Machine learning training data redundancy** is a widespread issue that can significantly impact the accuracy and efficiency of machine learning models. ##

What is Data Redundancy?

Data redundancy refers to the presence of duplicate or unnecessary information in a dataset. This can manifest in various forms, such as: * **Duplicate records**: Repeating the same information in multiple instances * **Redundant attributes**: Having multiple attributes that contain similar information * ** Correlated variables**: Variables that are highly correlated with each other, making one or both of them redundant ##

The Consequences of Data Redundancy in Machine Learning

Data redundancy can lead to several negative consequences in machine learning, including: * **Overfitting**: The model becomes too complex and starts to fit the noise in the data, rather than the underlying patterns * **Reduced accuracy**: Redundant data can lead to poor model performance and reduced accuracy * **Increased training time**: Dealing with redundant data can slow down the training process ##

Estimating the Degree of Redundancy in Machine Learning Data

;Machine Learning Training Data Redundancy

Estimating the degree of redundancy in machine learning data is crucial to address this issue. Several techniques can be used, including: * **Chi-square test**: A statistical test to identify redundant attributes * **Covariance-and-correlation analysis**: Identifying highly correlated variables * **Data normalization**: Removing redundant attributes or records ##

The Impact of Redundancy on Model Evaluation

Redundancy can skew the performance evaluation of machine learning models when using random splitting, leading to overestimated predictive performance and poor performance on out-of-sample data. This highlights the need to carefully evaluate the redundancy in the data and address it before model evaluation. ##

Techniques for Addressing Data Redundancy in Machine Learning

Several techniques can be used to address data redundancy in machine learning, including: * **Data normalization**: Organizing data, reducing redundancy, and improving integrity * **Learning-based methods**: Learning data redundancy on some data samples and applying the knowledge at runtime execution of the model * **Granular data provenance**: Establishing data provenance and implementing intelligent reuse strategies to efficiently eliminate redundant computations ##

Best Practices for Minimizing Data Redundancy

To minimize data redundancy in machine learning, follow these best practices: * **Document data creation**: Understand the origin and purpose of each attribute or record * **Audit and clean data**: Regularly review and clean data to remove duplicates and redundant information * **Use efficient data storage**: Utilize efficient data storage systems to minimize data redundancy In conclusion, **machine learning training data redundancy** is a significant issue that can impact the accuracy and efficiency of machine learning models. By understanding the consequences of redundancy, estimating its degree, and addressing it through various techniques, we can improve the quality of our machine learning models and achieve better results. Following best practices for minimizing data redundancy can also help to ensure that our models perform optimally and effectively.

📁 Category: Redundancy

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Exploiting redundancy in large materials datasets for efficient machine ...

Bigdatais crucial formachinelearning, but the redundancies in the datasets are rarely studied. Here the authors reveal significantredundancyin large materials datasets, showing that up to 95 ...

source: https://www.nature.com

Minimizing Redundancy and Data Requirements of Machine Learning ...

Jul 29, 2024Themachinelearningpotential has emerged as a promising approach for addressing the accuracy-versus-efficiency dilemma in molecular modeling. Efficiently exploring chemical spaces with high accuracy presents a significant challenge, particularly for the interface reaction system. This study introduces a workflow aimed at achieving this goal by incorporating the classical SOAP descriptor and ...

source: https://pubs.acs.org

Robust ML model ensembles via risk-driven anti-clustering of training data

In this paper, we improve the robustness ofMachineLearning(ML) classifiers againsttraining-time attacks by linking the risk oftrainingdatabeing tampered with to theredundancyin the ML model's design needed to prevent it.

source: https://www.sciencedirect.com

On the redundancy in large material datasets: efficient and robust ...

Extensive efforts to gather materialsdatahave largely overlooked potentialdataredundancy. In this study, we present evidence of a significant degree ofredundancyacross multiple large datasets for various material properties, by revealing that up to 95 % ofdatacan be safely removed frommachinelearningtrainingwith little impact on in-distribution prediction performance. The redundant ...

source: https://arxiv.org

Exploiting redundancy in large materials datasets for efficient machine ...

Abstract Extensive efforts to gather materialsdatahave largely overlooked potentialdataredundancy. In this study, we present evidence of a significant degree ofredundancyacross multiple large datasets for various material properties, by revealing that up to 95% ofdatacan be safely removed frommachinelearningtrainingwith little impact on in-distribution prediction performance. The ...

source: https://pmc.ncbi.nlm.nih.gov

Survey: Exploiting Data Redundancy for Optimization of Deep Learning

Learning-based Methods: These methods learndataredundancyon somedatasamples (often sampled from thetrainingor validation datasets) and apply the knowledge at runtime execution of the model.

source: https://dl.acm.org

Exploiting redundancy in large materials datasets for efficient machine ...

Extensive eforts to gather materialsdatahave largely overlooked potentialdataredundancy. In this study, we present evidence of a significant degree ofredundancyacross multiple large datasets for various material properties, by revealing that up to 95 % ofdatacan be safely removed fromma-chinelearningtrainingwith little impact on in-distribution prediction performance. The redundant ...

source: https://tsapps.nist.gov

paper-data-redundancy/README.md at main - GitHub

In this study, we present evidence of a significant degree ofredundancyacross multiple large datasets for various material properties, by revealing that up to 95 % ofdatacan be safely removed frommachinelearningtrainingwith little impact on in-distribution prediction performance.

source: https://github.com

MD-HIT: Machine learning for material property prediction with dataset ...

Oct 18, 2024Thisredundancyskews the performance evaluation ofmachinelearning(ML) models when using random splitting, leading to overestimated predictive performance and poor performance on out-of ...

source: https://www.nature.com

Redundancy and Correlation in Data Mining - GeeksforGeeks

Jul 28, 2025Prerequisites: Chi-square test, covariance-and-correlation What isDataRedundancy? Duringdataintegration indatamining, variousdatastores are used. This can lead to the problem ofredundancyindata. An attribute (column or feature ofdataset) is called redundant if it can be derived from any other attribute or set of attributes. Inconsistencies in attribute or dimension naming can ...

source: https://www.geeksforgeeks.org

Data Normalization Explained: Types, Examples, & Methods

Datanormalization organizesdata, reducesredundancy, and improves integrity. See types, examples, and methods for better analytics andmachinelearning.

source: https://risingwave.com

Data Workflow Acceleration: A Smart System for Redundancy Elimination ...

Dec 17, 2025This paper presents a novel framework designed to significantly accelerate these pipelines. By establishing granulardataprovenance and implementing intelligent reuse strategies, our system efficiently identifies and eliminates redundant computations. This approach tackles key challenges such as managing extensivedatatraces and accommodating non-deterministic operations through advanced ...

source: https://irojournals.com

Data center - Wikipedia

Adatacenter is a facility used to house computer systems and associated components, such as telecommunications and storage systems. [1][2]Datacenters are critical infrastructure for the storage and processing of information, and they support the global financial system, cloud services,machinelearning, and artificial intelligence. [3][4]

source: https://en.wikipedia.org

R2-Dreamer: Redundancy-Reduced World Models without Decoders or ...

2 days agoA central challenge in image-based Model-Based ReinforcementLearning(MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction-based methods often waste capacity on large task-irrelevant regions. Decoder-free methods instead learn robust representations by leveragingDataAugmentation (DA), but reliance on such ...

source: https://arxiv.org

SHAPCA: Consistent and Interpretable Explanations for Machine Learning ...

1 day agoThis study proposes an interpretablemachine-learningpipeline for spectroscopicdatathat integrates PCA, supervised classification, and SHAP-based explainability.

source: https://arxiv.org

;Machine Learning Training Data Redundancy

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