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Oct. 2013 (vol. 25 no. 10)
pp. 2356-2366
H. Altay Guvenir, Bilkent University, Ankara
Murat Kurtcephe, Case Western Reserve University, Cleveland
In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a single categorical feature, we show the necessary and sufficient condition that any ranking function must satisfy to achieve the maximum AUC. We also sketch a method to discretize a continuous feature in a way to reach the maximum AUC as well. RIMARC uses a heuristic to extend this maximization to all features of a data set. The ranking function learned by the RIMARC algorithm is in a human-readable form; therefore, it provides valuable information to domain experts for decision making. Performance of RIMARC is evaluated on many real-life data sets by using different state-of-the-art algorithms. Evaluations of the AUC metric show that RIMARC achieves significantly better performance compared to other similar methods.
Index Terms:
Training,Nickel,Algorithm design and analysis,Machine learning algorithms,Machine learning,Measurement,Training data,machine learning,Training,Nickel,Algorithm design and analysis,Machine learning algorithms,Machine learning,Measurement,Training data,decision support,Ranking,data mining
Citation:
H. Altay Guvenir, Murat Kurtcephe, "Ranking Instances by Maximizing the Area under ROC Curve," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2356-2366, Oct. 2013, doi:10.1109/TKDE.2012.214
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