Regularized F-measure maximization for feature selection and classification.
Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications. It gained much popularity in medical and other fields including biological markers and, diagnostic test. This is particularly due to the fact that in real-world problems misclassification costs are not known, and thus, ROC curve and related utility functions such as F-measure can be more meaningful performance measures. F-measure combines recall and precision into a global measure. In this paper, we propose a novel method through regularized F-measure maximization. The proposed method assigns different costs to positive and negative samples and does simultaneous feature selection and prediction with L(1) penalty. This method is useful especially when data set is highly unbalanced, or the labels for negative (positive) samples are missing. Our experiments with the benchmark, methylation, and high dimensional microarray data show that the performance of proposed algorithm is better or equivalent compared with the other popular classifiers in limited experiments.
Division of Biostatistics, University of Maryland Greenebaum Cancer Center, Baltimore, MD 21201, USA. zliu@umm.edu
Zhenqiu
Z
Liu
Ming
M
Tan
Feng
F
Jiang
D000465:Algorithms; D001185:Artificial Intelligence; D019175:DNA Methylation; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012372:ROC Curve