In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in continuously. Therefore, if the model does not learn from the new data, the model may not perform as well with the new data especially when the model's training data is different from the test data. The new framework proposed in this paper rebuilds the classifier model using selected data from test data set to improve the accuracy of the model. A case study on intrusion detection data set has been performed to evaluate our methodology. The result shows that this approach lead to have more accurate classification models over time.