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ROC‐Based Utility Function Maximization for Feature Selection and Classification with Applications to High‐Dimensional Protease Data
Author(s) -
Liu Zhenqiu,
Tan Ming
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01015.x
Subject(s) - computer science , maximization , feature selection , receiver operating characteristic , nonparametric statistics , sigmoid function , support vector machine , data mining , machine learning , generalization , sensitivity (control systems) , artificial intelligence , matlab , feature (linguistics) , function (biology) , mathematical optimization , mathematics , statistics , artificial neural network , mathematical analysis , linguistics , philosophy , electronic engineering , evolutionary biology , engineering , biology , operating system
Summary In medical diagnosis, the diseased and nondiseased classes are usually unbalanced and one class may be more important than the other depending on the diagnosis purpose. Most standard classification methods, however, are designed to maximize the overall accuracy and cannot incorporate different costs to different classes explicitly. In this article, we propose a novel nonparametric method to directly maximize the weighted specificity and sensitivity of the receiver operating characteristic curve. Combining advances in machine learning, optimization theory, and statistics, the proposed method has excellent generalization property and assigns different error costs to different classes explicitly. We present experiments that compare the proposed algorithms with support vector machines and regularized logistic regression using data from a study on HIV‐1 protease as well as six public available datasets. Our main conclusion is that the performance of proposed algorithm is significantly better in most cases than the other classifiers tested. Software package in MATLAB is available upon request.