The paper proposes a methodology called OP-KNN, which builds a one hidden-layer feed forward neural network, using nearest neighbors neurons with extremely small computational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multi-response sparse regression (MRSR) is used as the second step in order to rank each k-th nearest neighbor and finally as a third step leave-one-out estimation is used to select the number of neighbors and to estimate the generalization performances. This new methodology is tested on a toy example and is applied to financial modeling.
['Qi Yu', 'Antti Sorjamaa', 'Yoan Miche', 'Amaury Lendasse', 'Eric Séverin', 'Alberto Guillén', 'F. Mateo']
Optimal Pruned K-Nearest Neighbors: OP-KNN Application to Financial Modeling