Fig. 1

The TRL-FM framework. The framework for knowledge transfer using functional mapping and classification rules works as follows. First, use a feature selector to select relevant variables from the source and target datasets. Second, combine the selected variables into a single list and partition them into functional modules (FMs). Third, using the discovered functional modules in addition to rules induced from the source data, build a prior hypothesis of classification rules. Finally, using the prior hypothesis as a seed, learn a new classification rule model on the target data