A multivariate feature selection framework for high dimensional biomedical data classification

High dimensional biomedical data are becoming common in various predictive models developed for disease diagnosis and prognosis. Extracting knowledge from high dimensional data which contain a large number of features and a small sample size presents intrinsic challenges for classification models. Genetic Algorithms can be successfully adopted to efficiently search through high dimensional spaces, and multivariate classification methods can be utilized to evaluate combinations of features for constructing optimized predictive models. This paper proposes a framework which can be adopted for building prediction models for high dimensional biomedical data. The proposed framework comprises of three main phases. The feature filtering phase which filters out the noisy features; the feature selection phase which is based on multivariate machine learning techniques and the Genetic Algorithm to evaluate the filtered features and select the most informative subsets of features for achieving maximum classification performance; and the predictive modeling phase during which machine learning algorithms are trained on the selected features to construct a reliable prediction model. Experiments were conducted using four high dimensional biomedical datasets including protein and gene expression data. The results revealed optimistic performances for the multivariate selection approaches which utilize classification measurements based on implicit assumptions