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Genetic programming based ensemble system for microarray data classification.

Kun-Hong Liu1, Muchenxuan Tong2, Shu-Tong Xie3

  • 1Software School of Xiamen University, Xiamen, Fujian 361005, China ; Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong.

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A new genetic programming ensemble system (GPES) effectively classifies cancers using microarray data. This machine learning approach improves accuracy through evolutionary processes and feature selection, outperforming other ensemble methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Machine learning techniques are increasingly used for analyzing complex microarray data.
  • Accurate cancer classification from gene expression data remains a significant challenge.

Purpose of the Study:

  • To introduce a novel genetic programming (GP) based ensemble system (GPES) for effective cancer classification.
  • To enhance the accuracy and diversity of ensemble models through evolutionary computation.

Main Methods:

  • Utilized genetic programming (GP) to evolve ensemble systems composed of decision tree base classifiers.
  • Implemented Min, Max, and Average operators within the ensemble framework.
  • Employed feature selection and balanced subsampling techniques to boost ensemble diversity.
  • Employed a forward search algorithm for automatic selection of the final ensemble committee.

Main Results:

  • The proposed GPES demonstrated superior performance in classifying various cancer types across multiple microarray datasets.
  • GPES achieved better results compared to several existing ensemble systems in most evaluated cases.
  • The evolutionary process in GP inherently improved the accuracy of the ensemble systems.

Conclusions:

  • The GPES offers a powerful and effective machine learning framework for cancer classification using microarray data.
  • Further improvements in GPES performance are possible through the use of more sophisticated base classifiers or advanced sampling techniques.