Localized Feature Selection for Classification

Abstract

The main idea of this thesis is to present the novel concept of localized feature selection (LFS) for data classification and its application for coma outcome prediction.

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this study we propose a novel localized feature selection approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated localized classification method is also proposed.

The proposed LFS method selects a feature subset such that, within a localized region, within-class and between-class distances are respectively minimized and maximized. We first determine the localized region using an iterative procedure based on the distances in the original feature space. This results in a linear programming optimization problem. Then, the second method is formulated as a non-linear joint convex/increasing quasi-convex optimization problem where a logistic function is applied to focus the optimization process on the localized region within the unknown co-ordinate system. This results in a more accurate classification performance at the expense of some sacrifice in computational time. Experimental results on synthetic and real-world data sets demonstrate the effectiveness of the proposed localized approach.

Using the LFS idea, we propose a practical machine learning approach for automatic and continuous assessment of event related potentials for detecting the presence of the mismatch negativity component, whose existence has a high correlation with coma awakening. This process enables us to determine prognosis of a coma patient. Experimental results on normal and comatose subjects demonstrate the effectiveness of the proposed method.