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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Text classification performance: is the sample size the only factor to be considered?

Rosa L Figueroa1, Qing Zeng-Treitler

  • 1Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Chile.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

Predicting machine learning classifier performance in biomedical text mining is possible. This study develops a regression model using sample size and text features to estimate performance, reducing the need for new data annotations.

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Area of Science:

  • Biomedical informatics
  • Machine learning
  • Natural language processing

Background:

  • Text mining and supervised machine learning are increasingly used with biomedical databases.
  • Determining the optimal size of annotated training sets for machine learning classifiers remains a challenge.

Purpose of the Study:

  • To develop a regression model that predicts the performance of machine learning classifiers.
  • To investigate the influence of sample size and intrinsic text characteristics on classifier performance.

Main Methods:

  • Utilizing active learning in medical text classification.
  • Analyzing text features such as vocabulary size and document length.
  • Creating a regression model to predict classifier performance based on these features.

Main Results:

  • Prior research indicated that sample size and text characteristics impact classifier performance.
  • The developed regression model aims to predict performance using these identified features.
  • The study hypothesizes that performance can be predicted without new data annotations.

Conclusions:

  • A predictive model for machine learning classifier performance in biomedical text analysis is feasible.
  • This approach could optimize the creation of training datasets.
  • Reducing annotation requirements can streamline the development of biomedical text mining tools.