DATA IMBALANCE AND SAMPLING TECHNQUES

Registration ID: IJNRD_221328

Published ID: IJNRD2405320

DOI: Click Here to Get

Krishna Kuamr Joshi , Sandali Jain , Sudhanshu Yadav

Data imbalance, machine learning, skewed distribution, biased models, sampling techniques, oversampling, under sampling, hybrid methods, SMOTE, ADASYN, ensemble methods, model accuracy, challenges, overfitting, information loss, computational overhead, recent developments, trends, open research areas, robust solutions, model generalization.

Data imbalance in datasets is a pervasive challenge in machine learning and data analysis, where certain classes or categories are significantly underrepresented compared to others. This imbalance can lead to biased model training, affecting the performance and reliability of machine learning algorithms. Addressing data imbalance is crucial for achieving accurate and fair predictive models across various domains such as healthcare, finance, and fraud detection. Sampling techniques play a vital role in managing data imbalance by either oversampling the minority class, under sampling the majority class, or employing hybrid methods that combine both approaches. Oversampling techniques such as SMOTE generate synthetic instances of the minority class to balance the dataset, while under sampling methods randomly reduce instances from the majority class. Hybrid techniques seek a balance between generating synthetic samples and removing instances strategically to maintain the dataset's overall distribution. However, applying sampling techniques requires careful consideration of their impact on model generalization, potential overfitting risks, and computational overhead. Advanced methods like ensemble-based sampling and adaptive sampling algorithms such as ADASYN offer promising avenues to address these challenges effectively. Continued research and development in sampling techniques are essential to ensure robust, scalable, and unbiased machine learning models in real-world applications plagued by data imbalance.

Published Paper   E-Certificate

Krishna Kuamr Joshi, Sandali Jain, & Sudhanshu Yadav (May-2024). DATA IMBALANCE AND SAMPLING TECHNQUES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), d165-d174. https://ijnrd.org/papers/IJNRD2405320.pdf

Volume 9 Issue 5, May-2024

Pages : d165-d174

Paper Reg. ID: IJNRD_221328

Published Paper Id: IJNRD2405320

Downloads: 000121975

Research Area: Engineering

Country: Lucknow, UTTAR PRADESH, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405320.pdf

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