Data Classification with Modified Density Weighted Distance Measure for Diffusion Maps

Object-to-group probabilistic distance measure for uncertain data classification

Uncertain objects, where each feature is represented by multiple observations or a given or fitted probability density function, arise in applications such as sensor networks, moving object databases and medical and biological databases. We propose a methodology to classify uncertain objects based on a new probabilistic distance measure between an uncertain object and a group of uncertain objec...

A Method for Defuzzication by Weighted Distance

A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR

The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...

A Hierarchical Tree Distance Measure for Classification

In this paper, we explore the problem of classification where class labels exhibit a hierarchical tree structure. Many multiclass classification algorithms assume a flat label space, where hierarchical structures are ignored. We take advantage of hierarchical structures and the interdependencies between labels. In our setting, labels are structured in a product and service hierarchy, with a foc...

Diffusion maps for changing data

Graph Laplacians and related nonlinear mappings into low dimensional spaces have been shown to be powerful tools for organizing high dimensional data. Here we consider a data set X in which the graph associated with it changes depending on some set of parameters. We analyze this type of data in terms of the diffusion distance and the corresponding diffusion map. As the data changes over the par...