Embedded feature selection can be performed by analyzing the variables used in a Random Forest. Such a multivariate selection takes into account the interactions between variables but is not straightforward to interpret in a statistical sense. We propose a statistical procedure to measure variable importance that tests if variables are significantly useful in combination with others in a forest. We show experimentally that this new importance index correctly identifies relevant variables. The top of the variable ranking is largely correlated with Breiman's importance index based on a permutation test. Our measure has the additional benefit to produce p-values from the forest voting process. Such p-values offer a very natural way to decide which features are significantly relevant while controlling the false discovery rate. Practical experiments are conducted on synthetic and real data including low and high-dimensional datasets for binary or multi-class problems. Results show that the proposed technique is effective and outperforms recent alternatives by reducing the computational complexity of the selection process by an order of magnitude while keeping similar performances.
This paper introduces two feature selection methods to deal with heterogeneous data that include continuous and categorical variables. We propose to plug a dedicated kernel that handles both kinds of variables into a Recursive Feature Elimination procedure using either a non-linear SVM or Multiple Kernel Learning. These methods are shown to offer state-of-the-art performances on a variety of high-dimensional classification tasks.
We studied the interaction of the thymic hormone thymosin alpha 1 with peripheral blood B and T lymphocytes in patients with myasthenia gravis (MG), using antibodies against thymosin alpha 1 in an immunofluorescence technique. Eleven of 16 patients with symptomatic MG had an increased number of T lymphocytes bearing surface thymosin alpha 1 (T alpha 1); 5 patients with asymptomatic disease had normal levels of T alpha 1. In six young adults with symptomatic MG who subsequently responded to thymectomy, the number of T alpha 1 cells returned to normal 1 month after thymectomy. Because levels of T alpha 1 correlated with symptoms and thymosin alpha 1 specifically recruits helper T cells, our findings suggest that T alpha 1 may play an immunoregulatory role in the pathogenesis of MG. Determination of T alpha 1 levels may prove to be helpful in assessing residual thymic activity after thymectomy.
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