Fuzzy Granular Hyperplane Classifiers

Various Hyperplane Classifiers Using Kernel Feature Spaces

In machine learning the classification approach may be linear or nonlinear, but it seems that by using the so-called kernel idea, linear methods can be readily generalized to the nonlinear ones. The key idea was originally presented in Aizermann’s paper [1] and it was successfully renewed in the context of the ubiquitous Support Vector Machines (SVM) [2]. The roots of SV methods can be traced b...

Towards Granular Computing: Classifiers Induced From Granular Structures

Granular computing as a paradigm is an area frequently studied within the Approximate Reasoning paradigm. Proposed by L. A.Zadeh granular computing has been studied within fuzzy as well as rough set approaches to uncertainty. It is manifest that both theories are immanently related to granulation as fuzzy set theory begins with fuzzy membership functions whose inverse images are prototype granu...

Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data

Molecular profiling studies can generate abundance measurements for thousands of transcripts, proteins, metabolites, or other species in, for example, normal and tumor tissue samples. Treating such measurements as features and the samples as labeled data points, sparse hyperplanes provide a statistical methodology for classifying data points into one of two categories (classification and predic...

Granular Enhancement of Fuzzy ART/SOM Neural Classifiers Based on Lattice Theory

Genetic granular classifiers in modeling software quality

Hyperbox classifiers are one of the most appealing and intuitively transparent classification schemes. As the name itself stipulates, these classifiers are based on a collection of hyperboxes – generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form “if feature1 is in [a,b] and feature2 i...