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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Incremental Ant-Miner Classifier for Online Big Data Analytics.

Amal Al-Dawsari1, Isra Al-Turaiki2, Heba Kurdi1,3

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

The new Incremental Ant-Miner (IAM) algorithm efficiently analyzes dynamic big data from Internet of Things (IoT) environments. IAM offers faster, resource-saving online predictions compared to traditional models, outperforming benchmarks in accuracy and efficiency.

Keywords:
IoTant colony optimizationassociation rule miningbig data analyticsincremental classifiermachine learning

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Area of Science:

  • Machine Learning
  • Big Data Analytics
  • Internet of Things (IoT)

Background:

  • Internet of Things (IoT) environments generate vast, dynamic datasets challenging traditional machine learning models.
  • Retraining offline (static) models on entire datasets as new data arrives is resource- and time-intensive.
  • Online (incremental) machine learning models are crucial for instantaneous analysis of evolving big data.

Purpose of the Study:

  • To introduce the Incremental Ant-Miner (IAM), an online machine learning algorithm for efficient big data prediction.
  • To address the time and space overheads associated with classic offline classifiers in dynamic environments.
  • To demonstrate IAM's capability for timely, space-efficient predictions with high accuracy.

Main Methods:

  • Developed the Incremental Ant-Miner (IAM) algorithm, an adaptation of the Ant-Miner algorithm for online learning.
  • Evaluated IAM on six diverse datasets: horse colic, credit cards, flags, ionosphere, and two breast cancer datasets.
  • Compared IAM's performance against ten state-of-the-art classifiers, including naive Bayes, logistic regression, SVM, and random forest.

Main Results:

  • IAM significantly outperformed all benchmarked classifiers across nearly all performance metrics (accuracy, precision, recall, F-measure).
  • IAM requires retraining only on new data increments, drastically reducing time and resource consumption compared to full dataset retraining.
  • Experimental results confirm IAM's superiority in efficiency and predictive performance for dynamic big data scenarios.

Conclusions:

  • The Incremental Ant-Miner (IAM) classifier is highly effective for big data analytics in dynamic environments like IoT.
  • IAM offers a time- and resource-efficient solution for online prediction, maintaining high accuracy.
  • IAM demonstrates strong potential for applications requiring timely and efficient analysis of continuously arriving data.