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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

MILIS: multiple instance learning with instance selection.

Zhouyu Fu1, Antonio Robles-Kelly, Jun Zhou

  • 1Gippsland School of IT, Faculty of Information Technology, Monash University, Building 4N, Northways Road, Churchill, Victoria 3842, Australia. zhouyu.fu@monash.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces MILIS, an efficient algorithm for multiple instance learning (MIL) that speeds up training by adaptively selecting instances. It ensures performance is maintained while reducing computational complexity in large datasets.

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Area of Science:

  • Machine Learning
  • Computer Science

Background:

  • Multiple Instance Learning (MIL) classifies data bags containing instances.
  • MIL complexity increases with the number of instances, necessitating efficient training methods.

Purpose of the Study:

  • To address the challenge of large instance spaces in MIL.
  • To develop an efficient instance selection technique for MIL algorithms.

Main Methods:

  • Proposed MILIS, a novel MIL algorithm featuring adaptive instance selection.
  • Employed an alternating optimization framework to intertwine instance selection and classifier learning.
  • Utilized a kernel density estimator on negative instances for initial selection.

Main Results:

  • MILIS demonstrates improved efficiency in speeding up the training process.
  • The proposed approach maintains performance despite instance reduction.
  • Experimental results show superior utility and efficiency compared to existing methods.

Conclusions:

  • MILIS offers an effective solution for instance selection in MIL.
  • The algorithm converges and enhances computational efficiency for large-scale MIL problems.