Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins
It is well-known that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammar-based genetic programming (GGP) system to perform "algorithm construction". The GGP is used to build a complete rule induction algorithm tailored to 5 well-known UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with well-known human-designed rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the human-designed algorithms in discovering accurate rules predicting the minority class - whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class.
rule induction algorithms classification genetic programming postsynaptic proteins