Generalized Gini Correlation and its Application in Data-Mining
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An asymmetric correlation measure commonly used in social economics, called the Gini correlation, is defined between a numerical response and a rank. We generalize the definition of this correlation so that it can be applied to data mining. The new definition, called the generalized Gini correlation, is found to include special cases that are equivalent to common evaluation measures used in data mining, for example, the LIFT measures for a binary response and the expected profit measure for a monetary response. We consider estimation and inference regarding this generalized Gini correlation. The asymptotic distribution of the estimated correlation is derived with the help of some empirical process theory. We consider several ways of constructing confidence intervals and demonstrate their performance numerically. Our paper is interdisciplinary and makes contributions to both the Gini literature and the literature of statistical inference of performance measures in data mining.
KeywordsAsymptotic distribution Confidence interval Data mining Empirical process Gini correlation LIFT measures
Mathematics Subject Classification62E20 62P99
We thank Professor Shlomo Yitzhaki and Professor Edna Schechtman for their comments and suggestions on a previous draft of this paper. We also thank Professor Hongmei Jiang for introducing to us the literature on Gini correlation. Finally, we thank the editor and referees for their comments and suggestions which greatly improved this paper.
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