The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Pattern classification has been successfully applied in many problem domains, such as
biometric recognition, document classification or medical diagnosis. Missing or unknown …

Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in
today's society. Previous studies assess which anatomical parameters of the optic nerve can …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

The growing information infrastructure in a variety of disciplines involves an increasing
requirement for efficient data mining techniques. Fast dimensionality reduction methods are …

In targeted display advertising, the goal is to identify the best opportunities to display a
banner ad to an online user who is most likely to take a desired action such as purchasing a …

Accurate prediction in the presence of large number of missing values in the data set has
always been a challenging problem. Most of hybrid models to address this challenge have …

Clustering on multi-view data has attracted much more attention in the past decades. Most
previous studies assume that each instance appears in all views, or there is at least one …

Background As depression is the leading cause of disability worldwide, large-scale surveys
have been conducted to establish the occurrence and risk factors of depression. However …

Traditional supervised data classification considers only physical features (eg, distance or
similarity) of the input data. Here, this type of learning is called low level classification. On …