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Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT.

Dantong Wang1, Simon Fong1, Raymond K Wong2

  • 1Department of Computer and Information Science, Univeristy of Macau, SAR, Macau.

Scientific Reports
|February 24, 2017
PubMed
Summary
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This study introduces a new algorithm, ODR-ioVFDT, for handling outliers in bioinformatics data streams. It improves classification accuracy and efficiency in analyzing biological data for better disease diagnosis.

Area of Science:

  • Bioinformatics
  • Data Mining
  • Machine Learning

Background:

  • Outlier detection in streaming data is crucial for accurate bioinformatics analysis.
  • Distinguishing noise from exceptions and deciding how to handle them presents a challenge.

Purpose of the Study:

  • To propose a novel algorithm, ODR-ioVFDT, for effective outlier management in continuous bioinformatics data learning.
  • To integrate outlier detection directly into the data learning process, unlike traditional separate steps.

Main Methods:

  • Developed ODR-ioVFDT, an algorithm combining outlier detection with an incrementally Optimized Very Fast Decision Tree.
  • Utilized an adaptive interquartile-range based method to set a tolerance threshold for identifying exceptional data points.
  • Integrated outlier handling within the training process to adaptively include or exclude data.

Related Experiment Videos

Main Results:

  • ODR-ioVFDT demonstrated superior performance compared to models without outlier detection.
  • Significant improvements were observed in classification accuracy and kappa statistics.
  • The algorithm showed reduced time consumption in processing bioinformatics datasets.

Conclusions:

  • ODR-ioVFDT offers an effective, integrated approach to outlier detection in bioinformatics streaming data.
  • The algorithm enhances the analysis of biological data, potentially leading to more effective disease diagnosis and treatment.