Transductive modeling with GA parameter optimization

While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. The model approximates the output value only for this input vector. However, deciding on the appropriate distance measure, on the number of nearest neighbors and on a minimum set of important features/variables is a challenge and is usually based on prior knowledge or exhaustive trial and test experiments. This paper proposes a genetic algorithm (GA) approach for optimizing these three factors. The method is tested on several datasets from UCI repository for classification tasks and results show that it outperforms conventional approaches. The drawback of this approach is the computational time complexity due to the presence of GA, which can be overcome using parallel computer systems due to the intrinsic parallel nature of the algorithm.