Classification using association rules has added a new dimension to the ongoing research for accurate classifiers. Over the years, a number of associative classifiers based on positive rules have been proposed in literature. The target of this paper is to improve classification accuracy by using both negative and positive class association rules without sacrificing performance. The generation of negative associations from datasets has been attacked from different perspectives by various authors and this has proved to be a very computationally expensive task. This paper approaches the problem of generating negative rules from a classification perspective, how to generate a sufficient number of high quality negative rules efficiently so that classification accuracy is enhanced. We adopt a simple variant of Apriori algorithm for this and show that our proposed classifier "associative classifier with negative rules"(ACN) is not only time-efficient but also achieves significantly better accuracy than four other state-of-the-art classification methods by experimenting on benchmark UCI datasets.