Configuration

The localized feature selection method has a set of user configurable paramaters that can be tweaked to get your desired functionality. For a full description of each parameter refer to the papers listed in the citations section. The parameters are:

• alpha: (default: 19) the maximum number of selected features for each representative point
• gamma: (default: 0.2) impurity level tolerance, controls proportion of out-of-class samples can be in local region
• tau: (default: 2) number of passes through the training set
• sigma: (default: 1) adjusts weightings for observations based on their distance, values greater than 1 result in lower weighting
• n_beta: (default: 20) number of beta values to test, controls the relative weighting of intra-class vs. inter-class distance in the objective function
• nrrp: (default: 2000) number of iterations for randomized rounding process
• knn: (default: 1) number of nearest neighbours to compare for classification