Exploiting the ensemble paradigm for stable feature selection: A case study on high-dimensional genomic data

Ensemble classification is a well-established approach that involves fusing the decisions of multiple predictive models. A similar “ensemble logic” has been recently applied to challenging feature selection tasks aimed at identifying the most informative variables (or features) for a given domain of interest. In this work, we discuss the rationale of ensemble feature selection and evaluate the effects and the implications of a specific ensemble approach, namely the data perturbation strategy. Basically, it consists in combining multiple selectors that exploit the same core algorithm but are trained on different perturbed versions of the original data. The real potential of this approach, still object of debate in the feature selection literature, is here investigated in conjunction with different kinds of core selection algorithms (both univariate and multivariate). In particular, we evaluate the extent to which the ensemble implementation improves the overall performance of the selection process, in terms of predictive accuracy and stability (i.e., robustness with respect to changes in the training data). Furthermore, we measure the impact of the ensemble approach on the final selection outcome, i.e. on the composition of the selected feature subsets. The results obtained on ten public genomic benchmarks provide useful insight on both the benefits and the limitations of such ensemble approach, paving the way to the exploration of new and wider ensemble schemes

Feature Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy