Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardising the classification accuracy. Moreover, we propose an algorithm called PN-GARC that deals with negative classification rules. Considering negated items in classification framework provides additional information describing the data and reduces the conflicts while classifying new objects. Nevertheless, there are a sheer number of rules when considering negated items. That is why, we will explore generic classification rules both negative and positive ones in order to study their behaviour and their usefulness on the studied datasets. A detailed description of IGARC method is presented, as well as the experimentation study on 12 benchmark datasets proving that it is highly competitive in terms of accuracy in comparison with popular classification approaches.