Positive selection and negative selection are the main artificial immune approaches. They can find application in any task where an automatic identification of incoming cases as self or non-self is needed. Both the approaches build the so-called detectors for protecting self cells, i.e. positive class objects. In a positive selection approach, detectors are able to recognize self objects, whereas the other approach produces detectors that eliminate non-self objects. The difference in construction of detectors causes that the choice of the approach may depend on the characteristic of the data under consideration.The goal of this paper is to provide a hybrid approach that is able to adapt to a given data set, producing thereby a classifier achieving the best performance. The adaptation is done twofold: (a) in the training phase, each candidate for a detector is automatically determined whether it will be used to build a self or non-self detector; (b) in the inner test phase, the best type of the classifier (i.e. pure positive selection, pure negative selection, positive-negative selection, negative-positive selection) is chosen.Results of experiments conducted on databases from the UCI Repository show that the combination of positive and negative selection approaches may give a higher classification accuracy.