Naive Bayes (simply NB) [12] has been widely used in machine learning and data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, researchers have made a substantial amount of effort to improve naive Bayes. The related research work can be broadly divided into two approaches: eager learning and lazy learning, depending on when the major computation occurs. Different from eager approach, the key idea for extending naive Bayes from the lazy approach is to learn a naive Bayes for each testing example. In recent years, some lazy extensions of naive Bayes have been proposed. For example, SNNB [18], LWNB [7], and LBR [19]. All are aiming at improving the classification accuracy of naive Bayes. In many real-world machine learning and data mining applications, however, an accurate ranking is more desirable than an accurate classification. Responding to this fact, we present a lazy learning algorithm called instance greedily cloning naive Bayes (simply IGCNB) in this paper. Our motivation is to improve naive Bayes? ranking performance measured by AUC [4, 14]. We experimentally tested our algorithm, using the whole 36 UCI datasets recommended by Weka [1], and compared it to C4.4 [16], NB [12], SNNB [18] and LWNB [7]. The experimental results show that our algorithm outperforms all the other algorithms used to compare significantly in yielding accurate ranking.