| Combining classifiers, a common way to improve the performance of classification, has gained popularity in recent years. By combining classifiers, one can take advantage of the diversity of the classifiers, while some defects of a classifier can be compensated for by other classifiers. In this dissertation, a classifier combination method, one specifically applicable to the combination of classifier outputs---commonly called fusion---is investigated. The proposed fusion method focuses on the combination of classifier outputs with the help of feature space information that is referred to as context. The basic assumption in the proposed method is that a context corresponds to a homogeneous region in the feature space. By dividing the feature space into a given number of homogeneous regions, one can identify the same number of contexts, then a different fusion process can be developed for each context, hence the name context-dependent fusion (CDF).The context-dependent fusion algorithm is an iterative method simultaneously clustering the feature space and learning optimal parameters for fusion. Although CDF has several advantages over previous methods, it is limited in that it is only valid for convex clusters with linearly separable classes. To mitigate the convex cluster assumption, a modified CDF using regularization, called context-dependent fusion with regularization (CDF-R), is formulated. By adding the regularization terms, not only does CDF-R achieve noise robustness, the main purpose of regularization, but the consequent clusters, which need not be convex, result in better performance than CDF. Although CDF-R is better at classification than CDF, the linear separability does not change. To completely remove the limitation, CDF is transformed to be non-linear, termed kernel-based context-dependent fusion (K-CDF). K-CDF adopts modified kernel methods to remove the restrictions of CDF and remedies some problems in the original kernel methods. K-CDF consists of three main components: dimension reduction, feature space clustering, and fusion. For each component, robust kernel fuzzy principal component analysis (RKF-PCA), kernel-based global fuzzy c-means (KG-FCM), and fuzzy support vector machine for noisy data (FSVM-N) are formulated, and which correspond to the robust variant of kernel PCA, kernel FCM, and fuzzy SVM, respectively. Although the three modifications were originated to address different shortcomings, one common purpose is to reduce the effect of nose, i.e., making the kernel methods noise-robust. By combining the three robust kernel methods, not only does K-CDF overcome the convex cluster assumption and linearly separable restriction, but it achieves noise robustness and better performance than previous methods. |
