Discovery of significant rules for classifying cancer diagnosis data

@article{Li2003DiscoveryOS,
  title={Discovery of significant rules for classifying cancer diagnosis data},
  author={Jinyan Li and Huiqing Liu and See-Kiong Ng and Limsoon Wong},
  journal={Bioinformatics},
  year={2003},
  volume={19 Suppl 2},
  pages={ii93-102}
}
METHODS AND RESULTS We introduce a new method to discover many diversified and significant rules from high dimensional profiling data. We also propose to aggregate the discriminating power of these rules for reliable predictions. The discovered rules are found to contain low-ranked features; these features are found to be sometimes necessary for classifiers to achieve perfect accuracy. The use of low-ranked but essential features in our method is in contrast to the prevailing use of an ad-hoc… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 19 references

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery • 1998
View 16 Excerpts
Highly Influenced

C4.5: Programs for Machine Learning

View 9 Excerpts
Highly Influenced

Bagging predictors

Machine Learning • 1996
View 7 Excerpts
Highly Influenced

Boolean Feature Discovery in Empirical Learning

Machine Learning • 1990
View 4 Excerpts
Highly Influenced

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