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A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for

Hongqiang Lyu1, Mingxi Wan2, Jiuqiang Han3

  • 1School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, PR China; School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, PR China.

Computers in Biology and Medicine
|August 30, 2017
PubMed
Summary
This summary is machine-generated.

A new Orthogonal MIC Feature Selection (OMICFS) method effectively removes irrelevant redundancy in biomedical data mining. OMICFS improves feature selection accuracy and computational efficiency compared to existing methods.

Keywords:
Biomedical data miningFilter feature selectionGram-Schmidt Orthogonalization (GSO)Maximal Information Coefficient (MIC)

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Area of Science:

  • Biomedical Data Mining
  • Bioinformatics
  • Machine Learning

Background:

  • Filter feature selection is crucial for biomedical data mining.
  • The minimal-Redundancy-Maximal-Relevance (mRMR) method faces challenges with irrelevant redundancy.
  • Existing mRMR enhancements, like KCCAmRMR, require additional procedures.

Purpose of the Study:

  • To propose a novel filter feature selection method, OMICFS, addressing irrelevant redundancy.
  • To enhance feature selection by optimizing max-relevance and min-redundancy criteria.
  • To improve classification accuracy and computational efficiency in biomedical data analysis.

Main Methods:

  • Utilizing Maximal Information Coefficient (MIC) for feature-target relevance.
  • Employing Gram-Schmidt Orthogonalization (GSO) to create orthogonalized features.
  • Indirectly optimizing relevance and redundancy by maximizing MIC with GSO-orthogonalized variables.

Main Results:

  • OMICFS effectively excludes irrelevant redundancy without extra steps.
  • Demonstrated superior performance in classification accuracy across biomedical datasets.
  • Showcased improved computational efficiency compared to other filter methods.

Conclusions:

  • OMICFS offers a robust solution for irrelevant redundancy in feature selection.
  • The method is suitable for mining high-dimensional biomedical data.
  • OMICFS presents a significant advancement in filter-based feature selection techniques.