| Abstract [eng] |
Nowadays, when the environment is changing rapidly and dynamically, there is a particular need for adaptive data mining methods. `Spam' filters, personalized recommender and marketing systems, network intrusion detection systems, business prediction and decision support systems need to be regularly retrained to take into account changing nature of the data. In the stationary settings the more data is at hand, the more accurate model can be trained. In the changing environment an old data decreases the accuracy. In such a case only a subset of the historical data might be selected to form a training set. For instance, the training window strategy uses only the newest historical instances. In the thesis adaptive data mining methods are addressed, which are based on selective training set formation. The thesis improves the training strategies under sudden, gradual and recurring concept drifts. Four adaptive training set formation algorithms are developed and experimentally validated, which allow to increase the generalization performance of the base models under each of the three concept drift types. Experimental evaluation using generated and real data confirms improvement of the classification and prediction accuracies as compared to using all the historical data as well as the selected existing adaptive learning algorithms from the recent literature. A tailored method for an industrial boiler application, which unifies several drift types, is developed. |
