🚀 Parallelized Extreme Learning Machine (P-ELM)

High-Performance DevOps Anomaly Detection & Online Classification

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📊 Performance Benchmark

Tested on High-Performance Infrastructure using the KDD Cup '99 dataset (~494k samples).

Metric	Result
Throughput	~345,000 samples processed in < 6s
Global Accuracy	98.2%
Anomaly Recall	99.1%
Parallel Efficiency	Distributed across 12-core CPU Architecture
Scalability	Linear scaling with Batch-based Parallelism

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🌟 Key Features (Springer 2022 Implementation)

This implementation strictly follows the architectural framework of the P-ELM paper published in Applied Intelligence (Springer):

• ⚡ SVD-Augmented Initialization: Unlike standard ELMs, this version uses Singular Value Decomposition on augmented data matrices to initialize both Weights and Biases, ensuring superior numerical stability.
• 🧠 Intelligent Knowledge Base (KB): Features a fixed-length KB buffer that stores high-performing model weights, filtering out noise through eligibility criteria.
• 🔄 Master-Worker Synthesis: Parallel workers compute local output weights which are then synthesized by a central Master node using Centrality-based Model Averaging.
• 🛡️ Online Evaluator: Real-time feedback loop that validates learning quality before updating the Knowledge Base.

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🏗️ Technical Architecture

Project Structure & Components

• src/elm_svd.py: Core ELM logic with SVD-based initialization for weights and biases.
• src/weight_synthesizer.py: Knowledge Base management and eligibility-based weight merging.
• src/elm_online.py: Parallel orchestration layer using joblib for multi-core distribution.
• Demo.ipynb: Interactive visualization and performance analytics dashboard.

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📖 Theoretical Background

This project implements the four main components of the P-ELM framework:

1. Parallel ELM Workers: Independent learners processing data chunks.
2. Weight Synthesizer: Aggregates knowledge from workers.
3. Knowledge Base (KB): Retains historical learning with a fixed-length memory.
4. Evaluator: ensures the reliability of newly learned patterns.

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Auto-Generated Benchmark Results

Last updated: 2026-06-18 09:47:48
System: Darwin arm64
Dataset: data/kddcup.data_10_percent.gz
Task: multi-class incident/attack-type classification
Samples used: 494,021 total, 395,216 train, 98,805 test
Features: 41 | Classes: 23
Configuration: hidden neurons = min(batch_size, feature_dim), batch = 2000, workers = 2, split = 80%/20%

Model Comparison

Model	Accuracy	Precision	Recall	F1	Train Time	Throughput	Peak CPU	Peak RAM
Sequential ELM	0.9935	0.9919	0.9935	0.9924	0.424s	932,769/s	29.8%	1444.2 MB
Sequential SVD-ELM	0.9935	0.9917	0.9935	0.9921	0.713s	554,485/s	23.9%	1748.1 MB
Online Parallel ELM	0.9941	0.9926	0.9941	0.9930	1.781s	221,893/s	16.0%	1743.5 MB

Best accuracy: Online Parallel ELM (0.9941)
Fastest training: Sequential ELM (0.424s)

Paper Batch-Size Sweep: Online Parallel ELM

Batch Size	Hidden Size	Accuracy	Precision	Recall	F1	Train Time	KB Vectors
200	41	0.9831	0.9774	0.9831	0.9793	18.794s	20
300	41	0.9860	0.9802	0.9860	0.9824	12.478s	20
400	41	0.9912	0.9877	0.9912	0.9893	9.386s	20
500	41	0.9911	0.9878	0.9911	0.9894	7.450s	20
1000	41	0.9923	0.9894	0.9923	0.9908	3.678s	20
2000	41	0.9941	0.9926	0.9941	0.9930	1.764s	20

Best sweep accuracy: batch size 2000 (0.9941)

📚 Reference

Based on the research paper:

Parallelized Extreme Learning Machine for Online Data Classification
Vidhya M. & Aji S. (2022)
Journal: Applied Intelligence, Springer.
DOI: 10.1007/s10489-022-03308-7

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Developed with ❤️ by Amanda Taheri