By Bernadete Ribeiro, Rudolf F. Albrecht, Andrej Dobnikar, David W. Pearson, Nigel C. Steele
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6. N. Lavraˇc, B. Kavˇsek, P. Flach and L. Todorovski. Subgroup discovery with CN2SD. Journal of Machine Learning Research, 5: 153–188, 2004. 7. J. Li and L. Wong. Geography of diﬀerences between two classes of data. In Proc. of 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2002), Springer, 325–337, 2002. 8. F. Provost and T. Fawcett. Robust classiﬁcation for imprecise environments. Machine Learning, 42(3): 203–231, 2001. 9. S. Ramaswamy et al. Multiclass cancer diagnosis using tumor gene expression signitures.
In this work, we found an appropriate tradeoﬀ between rule simplicity and the amount of supportive evidence by enabling the expert to inspect all the statistically signiﬁcant supporting factors, whereas the decision whether they indeed increase the user’s conﬁdence in the subgroup description is left to the expert. In the CHD application the expert has decided whether the proposed supporting factors are meaningful, interesting and actionable, how reliable they are and how easily they can be measured in practice.
Right) table. Each cluster represents the maximum co-regulated genes with the corresponding microarray identity : the cluster A (resp. a) of Table 2 represents 2 up-regulated (resp. 1 down-regulated) genes over 77 microarrays corresponding to three ﬁrst depths of Figure 5. These maximum co-regulated genes could be then considered as characteristic genes of breast microarrays. These results permit also to isolate the groups of no-regulated genes : from the depth 4 to the depth 12, 30 T. C. M. Hoang Table 2.
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