title: The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer.
abstract: Increasing interest in new pattern recognition methods has been motivated by bioinformatics research. The analysis of gene expression data originated from microarrays constitutes an important application area for classification algorithms and illustrates the need for identifying important predictors. We show that the Goodman-Kruskal coefficient can be used for constructing minimal classifiers for tabular data, and we give an algorithm that can construct such classifiers.
authors.affiliation: Department of Computer Science, University of Massachusetts, Boston, MA 02125, USA. sj@cs.umb.edu
authors.forename: Szymon
authors.initials: S
authors.lastname: Jaroszewicz
authors.forename: Dan A
authors.initials: DA
authors.lastname: Simovici
authors.forename: Winston P
authors.initials: WP
authors.lastname: Kuo
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005820:Genetic Testing / Q000379:methods; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity