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Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support.

Publication ,  Journal Article
Mazurowski, MA; Malof, JM; Tourassi, GD
Published in: Phys Med Biol
January 21, 2011

When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.

Duke Scholars

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

January 21, 2011

Volume

56

Issue

2

Start / End Page

473 / 489

Location

England

Related Subject Headings

  • Radiographic Image Interpretation, Computer-Assisted
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Humans
  • Decision Support Systems, Clinical
  • Databases, Factual
  • Breast Neoplasms
  • Algorithms
  • 5105 Medical and biological physics
 

Citation

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Mazurowski, M. A., Malof, J. M., & Tourassi, G. D. (2011). Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support. Phys Med Biol, 56(2), 473–489. https://doi.org/10.1088/0031-9155/56/2/012
Mazurowski, Maciej A., Jordan M. Malof, and Georgia D. Tourassi. “Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support.Phys Med Biol 56, no. 2 (January 21, 2011): 473–89. https://doi.org/10.1088/0031-9155/56/2/012.
Mazurowski, Maciej A., et al. “Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support.Phys Med Biol, vol. 56, no. 2, Jan. 2011, pp. 473–89. Pubmed, doi:10.1088/0031-9155/56/2/012.
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

January 21, 2011

Volume

56

Issue

2

Start / End Page

473 / 489

Location

England

Related Subject Headings

  • Radiographic Image Interpretation, Computer-Assisted
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Humans
  • Decision Support Systems, Clinical
  • Databases, Factual
  • Breast Neoplasms
  • Algorithms
  • 5105 Medical and biological physics