The 10th annual MLSP competition: Second place - Lebedev, Alexander V. (2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2014)
The goal of the MLSP 2014 Classification Challenge was to automatically detect subjects with schizophrenia and schizoaffective disorder based on multimodal features derived from the magnetic resonance imaging (MRI) data. The patients with age range of 18-65 years were diagnosed according to DSM-IV criteria. The training data consisted of 46 patients and 40 healthy controls. The test set included 119 748 subjects with unknown labels. In the present solution, we implemented so-called "feature trimming", consisting of: 1) introducing a random vector into the feature set, 2) calculating feature importance based on mean decrease of the Gini-index derived by running Random Forest classification, and 3) removing the features with importance below the "dummy variable". Support Vector Machine with Gaussian Kernel was used to run final classification with reduced feature set achieving test set AUC of 0.923.