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 …

Class imbalance and missing values are two critical problems in pattern classification.
Researchers have proposed a number of techniques to address each of the problems …

Datasets that have skewed class distributions pose a difficulty to learning algorithms in
pattern classification. A number of different methods to deal with this problem have been …

We study the question of how to represent or summarize raw laboratory data taken from an
electronic health record (EHR) using parametric model selection to reduce or cope with …

Database marketing (DBM) provides in-depth analysis of marketing databases. Knowledge
discovery in database techniques is one of the most prominent approaches to supporting …

An important challenge for many firms is to identify the life transitions of its customers, such
as job searching, expecting a child, or purchasing a home. Inferring such transitions, which …

Imputing missing values in imbalanced datasets remains an open challenge. Most methods
assume data are missing at random or follow a standard distribution, lacking robustness for …

The most important stage of data mining is pre-processing, where we prepare the data for
mining. Real-world data tends to be incomplete, noisy, and inconsistent and an important …

Class imbalance and missing value are significant problems in datasets. Various
researchers study both issues under a different scenario, but very few consider them …

Data migration is a critical process that directly influenced the quality of data management.
Data migration had affected on the quality of the data, such as, accuracy, data elements, and …