title: Discovery of significant rules for classifying cancer diagnosis data.
abstract: 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 number of only top-ranked features. On a wide range of data sets, our method displayed highly competitive accuracy compared to the best performance of other kinds of classification models. In addition to accuracy, our method also provides comprehensible rules to help elucidate the translation between raw data and useful knowledge.
authors.affiliation: Institute for Infocomm Research, Heng Mui Keng Terrace, Singapore. jinyan@i2r.a-star.edu.sg
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
authors.forename: Huiqing
authors.initials: H
authors.lastname: Liu
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins