Machine Learning
Hybrid Synthetic Minority Over-sampling Technique (HSMOTE) and Ensemble Deep Dynamic Classifier Model (EDDCM) for big data analytics
This paper introduces HSMOTE for robust class imbalance handling and EDDCM for enhanced classification in big data analytics. Integrating meta-heuristic optimization for feature selection, the framework achieves superior accuracy and generalization across various datasets.
Executive Impact: Key Metrics
Our analysis of Hybrid Synthetic Minority Over-sampling Technique (HSMOTE) and Ensemble Deep Dynamic Classifier Model (EDDCM) for big data analytics reveals critical performance enhancements. Here's a quick look at the projected impact for your enterprise.
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Abstract
Big Data Classification (BDC) faces challenges with high dimensionality and class imbalance, degrading conventional machine learning (ML) model performance. This study proposes a hybrid framework integrating meta-heuristic optimization with class imbalance handling. HSMOTE generates synthetic minority samples to improve rare class representation. The Optimization Ensemble Feature Selection Model (OEFSM) combines Fuzzy Weight Dragonfly Algorithm (FWDFA), Adaptive Elephant Herding Optimization (AEHO), and Fuzzy Weight Grey Wolf Optimization (FWGWO) for robust feature selection. The Ensemble Deep Dynamic Classifier Model (EDDCM) incorporates Density Weighted Convolutional Neural Network (DWCNN), Density Weighted Bi-Directional Long Short-Term Memory (DWBi-LSTM), and Weighted Autoencoder (WAE), aggregated using a dynamic ensemble strategy for reliable predictions. Implemented in MATLAB, the framework demonstrates improved classification results across various datasets.
Introduction
The increasing volume of data in various domains, including bioinformatics, health, marketing, and finance, presents significant challenges for traditional Data Mining (DM) and Machine Learning (ML) algorithms. High dimensionality and class imbalance are prevalent issues in Big Data Classification (BDC), often leading to suboptimal model performance. Deep Learning (DL) methods have shown promise in areas like Breast Cancer Detection due to their ability to extract hidden patterns with less human intervention than traditional ML. However, existing methods for feature selection (FS) and classification struggle with stability, accuracy, and adaptability to evolving data distributions. This study aims to address these critical gaps by proposing a novel hybrid framework.
Enterprise Process Flow
Data Collection
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Data Cleaning
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Class Imbalance Handling (HSMOTE)
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Feature Selection (OEFSM)
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Data Normalization
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Model Training
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Evaluation
99.89%
Overall Accuracy Achieved by EDDCM
| Method | Advantages | Disadvantages |
|---|---|---|
| SMOTE |
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| HSMOTE (Proposed) |
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| OEFSM (Proposed) |
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| EDDCM (Proposed) |
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Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A phased approach to integrate HSMOTE and EDDCM into your existing big data pipeline.
Phase 01: Assessment & Strategy
Goal: Understand current data landscape, identify key challenges (imbalance, dimensionality), and define success metrics for HSMOTE & EDDCM. Develop a tailored strategy.
Activities: Data audit, requirement gathering, architecture review, initial workshop with stakeholders.
Phase 02: Proof of Concept & Pilot
Goal: Implement HSMOTE and EDDCM on a subset of your data to demonstrate efficacy and validate performance gains. Refine models based on pilot results.
Activities: Data preprocessing with HSMOTE, OEFSM feature selection, EDDCM model training and evaluation on pilot data, iterative refinement.
Phase 03: Full-Scale Integration & Deployment
Goal: Integrate the optimized HSMOTE-OEFSM-EDDCM pipeline into your production environment, ensuring scalability and robust performance.
Activities: Production deployment, API integration, continuous monitoring setup, team training, documentation.
Phase 04: Optimization & Expansion
Goal: Continuously monitor model performance, identify opportunities for further optimization, and explore expansion to new use cases or datasets.
Activities: A/B testing, re-training with new data, feature engineering, exploring additional DL architectures, performance tuning.
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Leverage the power of HSMOTE and EDDCM to overcome class imbalance and high dimensionality in your big data analytics. Schedule a free consultation to see how our expertise can drive your enterprise forward.
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