An adaptive method for statistical detection with applications to drug discovery
classification, hit curve, kernel method, nearest neighbor
Researchers have tried to tackle various statistical detection problems using state-of-the-art classification techniques but are often disappointed at the results. The reason is two-fold. First of all, as classification problems, these statistical detection problems are heavily unbalanced: the class of interest is rare in the training data; an overwhelming majority of the training data belong to what can be called a background class. A primary example is drug discovery, where most of the chemical compounds in the data set are inactive whereas the goal is to detect a small number of active compounds. Secondly, the goal of statistical detection is fundamentally different from that of classification, making misclassification rate the wrong criterion to focus on. In this article, we develop an adaptive method for statistical detection and demonstrate that it can be an effective tool for drug discovery.
Zhu, Mu, H. A. Chipman, and W. Su. "An adaptive method for statistical detection with applications to drug discovery." In 2003 Proceedings of the American Statistical Association, Biopharmaceutical Section. Retrieved from http://sas.uwaterloo.ca/~m3zhu/papers/jsm2003.pdf
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